Accord
A delegate which is used by events notifying abount sent/received message.
Event sender.
Event arguments containing details about the transferred message.
Structure for representing a pair of coordinates of double type.
The structure is used to store a pair of floating point
coordinates with double precision.
Sample usage:
// assigning coordinates in the constructor
DoublePoint p1 = new DoublePoint( 10, 20 );
// creating a point and assigning coordinates later
DoublePoint p2;
p2.X = 30;
p2.Y = 40;
// calculating distance between two points
double distance = p1.DistanceTo( p2 );
X coordinate.
Y coordinate.
Initializes a new instance of the structure.
X axis coordinate.
Y axis coordinate.
Calculate Euclidean distance between two points.
Point to calculate distance to.
Returns Euclidean distance between this point and
points.
Calculate squared Euclidean distance between two points.
Point to calculate distance to.
Returns squared Euclidean distance between this point and
points.
Addition operator - adds values of two points.
First point for addition.
Second point for addition.
Returns new point which coordinates equal to sum of corresponding
coordinates of specified points.
Addition operator - adds values of two points.
First point for addition.
Second point for addition.
Returns new point which coordinates equal to sum of corresponding
coordinates of specified points.
Subtraction operator - subtracts values of two points.
Point to subtract from.
Point to subtract.
Returns new point which coordinates equal to difference of corresponding
coordinates of specified points.
Subtraction operator - subtracts values of two points.
Point to subtract from.
Point to subtract.
Returns new point which coordinates equal to difference of corresponding
coordinates of specified points.
Addition operator - adds scalar to the specified point.
Point to increase coordinates of.
Value to add to coordinates of the specified point.
Returns new point which coordinates equal to coordinates of
the specified point increased by specified value.
Addition operator - adds scalar to the specified point.
Point to increase coordinates of.
Value to add to coordinates of the specified point.
Returns new point which coordinates equal to coordinates of
the specified point increased by specified value.
Subtraction operator - subtracts scalar from the specified point.
Point to decrease coordinates of.
Value to subtract from coordinates of the specified point.
Returns new point which coordinates equal to coordinates of
the specified point decreased by specified value.
Subtraction operator - subtracts scalar from the specified point.
Point to decrease coordinates of.
Value to subtract from coordinates of the specified point.
Returns new point which coordinates equal to coordinates of
the specified point decreased by specified value.
Multiplication operator - multiplies coordinates of the specified point by scalar value.
Point to multiply coordinates of.
Multiplication factor.
Returns new point which coordinates equal to coordinates of
the specified point multiplied by specified value.
Multiplication operator - multiplies coordinates of the specified point by scalar value.
Point to multiply coordinates of.
Multiplication factor.
Returns new point which coordinates equal to coordinates of
the specified point multiplied by specified value.
Division operator - divides coordinates of the specified point by scalar value.
Point to divide coordinates of.
Division factor.
Returns new point which coordinates equal to coordinates of
the specified point divided by specified value.
Division operator - divides coordinates of the specified point by scalar value.
Point to divide coordinates of.
Division factor.
Returns new point which coordinates equal to coordinates of
the specified point divided by specified value.
Equality operator - checks if two points have equal coordinates.
First point to check.
Second point to check.
Returns if coordinates of specified
points are equal.
Inequality operator - checks if two points have different coordinates.
First point to check.
Second point to check.
Returns if coordinates of specified
points are not equal.
Check if this instance of equal to the specified one.
Another point to check equalty to.
Return if objects are equal.
Get hash code for this instance.
Returns the hash code for this instance.
Explicit conversion to .
Double precision point to convert to integer point.
Returns new integer point which coordinates are explicitly converted
to integers from coordinates of the specified double precision point by
casting double values to integers value.
Explicit conversion to .
Double precision point to convert to single precision point.
Returns new single precision point which coordinates are explicitly converted
to floats from coordinates of the specified double precision point by
casting double values to float value.
Rounds the double precision point.
Returns new integer point, which coordinates equal to whole numbers
nearest to the corresponding coordinates of the double precision point.
Get string representation of the class.
Returns string, which contains values of the point in readable form.
Calculate Euclidean norm of the vector comprised of the point's
coordinates - distance from (0, 0) in other words.
Returns point's distance from (0, 0) point.
Specifies that a class can be used to decode a particular file type.
Gets or sets the file extension that this format decoder applies to (i.e. "wav").
Initializes a new instance of the class.
Populates the dictionary with available decoders of a particular category by
inspecting types from all referenced assemblies. Note: calling this method
will force all referenced assemblies to be loaded into the current AppDomain.
The base type for the decoders. This should be an interface such as IImageDecoder or IAudioDecoder.
The dictionary where the found decoders will be stored.
The extension we are interested in.
Specifies that an argument, in a method or function,
must be greater than zero.
Gets or sets the type of the source.
The type of the source.
Initializes a new instance of the class.
Specifies a serialization binder to be used whenever a class is
being deserialized by the framework. This can be used to ensure
binary compatibility when the framework code changes.
The binder to be used for the class marked with this attribute.
Initializes a new instance of the class.
The binder to be used to deserialize objects of this type.
Specifies a serialization surrogate to be used whenever a class is
being deserialized by the framework. This can be used to ensure
binary compatibility when the framework code changes.
The binder to be used for the class marked with this attribute.
Initializes a new instance of the class.
The surrogate selector to be used to deserialize objects of this type.
Tree node for binary trees.
The class type for the nodes of the tree.
Gets or sets the left subtree of this node.
Gets or sets the right subtree of this node.
Gets whether this node is a leaf (has no children).
Indicates whether the current object is equal to another object of the same type.
An object to compare with this object.
true if the current object is equal to the parameter; otherwise, false.
Gets or sets the collection of child nodes
under this node.
Base class for binary trees. This class does not implement a binary search
tree, but can used to implement one. For binary search trees, please refer
to , KDTree and VPTree.
The class is a base class for other tree classes
such as , KDTree and VPTree. For examples on how to
use those classes, please see their respective documentation pages.
If you would like to implement your own binary tree that inherits from this class,
then you can do so as shown in the following example. First, make sure your custom
node class inherits from and passes itself as the generic
argument of :
The MIT License (MIT)
Copyright (c) 2013 Daniel "BlueRaja" Pflughoeft
https://github.com/BlueRaja/High-Speed-Priority-Queue-for-C-Sharp
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Initializes a new instance of the class.
The initial capacity for the queue.
The sort order for the queue. If set to ,
items that have a smaller priority number are the next to be dequeued. If set to
, items with larger priority numbers are the ones
with higher priority and will be the next to be dequeued. Default is .
New queue size cannot be smaller than 1.
Gets the number of nodes in the queue. This is an O(1) operation.
Gets the current capacity of this queue.
Gets or sets the ordering of this priority queue.
Removes every node from the queue. This is an O(1) operation.
Returns whether the given node is in the queue. This is an O(1) operation.
Enqueue a node to the priority queue. Lower values are placed in front.
Ties are broken by first-in-first-out. This is an O(log n) operation.
Returns true if 'higher' has higher priority than 'lower', false otherwise. Note that
calling HasHigherPriority(node, node) (ie. both arguments the same node) will return false.
Removes the head of the queue (node with minimum priority; ties are broken by order
of insertion), and returns it. This is an O(log n) operation.
Resize the queue so it can accept more nodes. All currently enqueued nodes are kept.
Attempting to decrease the queue size to a size too small to hold the existing nodes
results in undefined behavior. This is an O(n) operation.
Returns the head of the queue, without removing it (use Dequeue() for that).
If the queue is empty, behavior is undefined. This is an O(1) operation.
Gets a value indicating whether this instance is read only (returns false).
Returns false, as instances of this class are not read only.
This method must be called on a node every time its priority changes while it is in the queue.
Forgetting to call this method will result in a corrupted queue!. This is an O(log n) operation.
Removes a node from the queue. The node does not need to be the head of the
queue. This is an O(log n) operation.
Returns an enumerator that iterates through the collection.
A that can be used to iterate through the collection.
Returns an array containing the items in this list,
optionally in in priority order.
Whether to return the items in priority order.
Checks to make sure the queue is still in a valid state.
Returns a that represents this instance.
A that represents this instance.
Represents the node of a priority queue.
The type for the values stored in the queue.
Initializes a new instance of the struct.
The value to store in the node.
A double value representing the priority for the node.
The index of the node in the priority queue.
The original index of the node in the priority queue, at time of first insertion.
Gets a double-value representing the
current priority for the node.
Gets or sets the current value associated with this node.
Gets the original position at which this node was inserted.
Gets the current position of this node in its containing queue.
Determines whether the specified , is equal to this instance.
The to compare with this instance.
true if the specified is equal to this instance; otherwise, false.
Returns a hash code for this instance.
A hash code for this instance, suitable for use in hashing algorithms and data structures like a hash table.
Gets a value indicating whether this node is empty (does not belong to any queue).
Indicates whether the current object is equal to another object of the same type.
An object to compare with this object.
true if the current object is equal to the parameter; otherwise, false.
Compares the current object with another object of the same type.
An object to compare with this object.
A value that indicates the relative order of the objects being compared. The return value has the following meanings: Value Meaning Less than zero This object is less than the parameter.Zero This object is equal to . Greater than zero This object is greater than .
Implements the equals operator.
Implements the not equals operator.
Implements the greater than operator.
Implements the less than operator.
Gets an instance representing an empty node.
Returns a that represents this instance.
A that represents this instance.
Ordered dictionary.
This class provides a ordered dictionary implementation for C#/.NET. Unlike the rest
of the framework, this class is available under a MIT license, so please feel free to
re-use its source code in your own projects.
The types of the keys in the dictionary.
The type of values in the dictionary.
Initializes a new instance of the class.
Initializes a new instance of the class.
The initial number of elements that the can contain.
Initializes a new instance of the class.
The IEqualityComparer implementation to use when comparing keys, or null to use
the default EqualityComparer for the type of the key.
Initializes a new instance of the class.
The initial number of elements that the can contain.
The IEqualityComparer implementation to use when comparing keys, or null to use
the default EqualityComparer for the type of the key.
Initializes a new instance of the class.
The System.Collections.Generic.IDictionary`2 whose elements are copied to the
new .
The IEqualityComparer implementation to use when comparing keys, or null to use
the default EqualityComparer for the type of the key.
Gets the at the specified index.
The index.
Gets the at the specified index.
The index.
Gets or sets the with the specified key.
The key.
Gets an containing the keys of the .
The keys.
Gets an containing the values in the .
Gets the number of elements contained in the .
Gets a value indicating whether the is read-only.
true if this instance is read only; otherwise, false.
Adds an element with the provided key and value to the .
The object to use as the key of the element to add.
The object to use as the value of the element to add.
Adds an item to the .
The object to add to the .
Removes all items from the .
Determines whether the contains a specific value.
The object to locate in the .
true if is found in the ; otherwise, false.
Determines whether the contains an element with the specified key.
The key to locate in the .
true if the contains an element with the key; otherwise, false.
Copies the elements of the to an , starting at a particular index.
The one-dimensional that is the destination of the elements copied from . The must have zero-based indexing.
The zero-based index in at which copying begins.
Returns an enumerator that iterates through the collection.
An enumerator that can be used to iterate through the collection.
Removes the element with the specified key from the .
The key of the element to remove.
true if the element is successfully removed; otherwise, false. This method also returns false if was not found in the original .
Removes the first occurrence of a specific object from the .
The object to remove from the .
true if was successfully removed from the ; otherwise, false. This method also returns false if is not found in the original .
Gets the value associated with the specified key.
The key whose value to get.
When this method returns, the value associated with the specified key, if the key is found; otherwise, the default value for the type of the parameter. This parameter is passed uninitialized.
true if the object that implements contains an element with the specified key; otherwise, false.
Returns an enumerator that iterates through a collection.
An object that can be used to iterate through the collection.
Base class for tree structures.
The class type for the nodes of the tree.
Gets the root node of this tree.
Returns an enumerator that iterates through the collection.
A that can be used to iterate through the collection.
Traverse the tree using a tree traversal
method. Can be iterated with a foreach loop.
The tree traversal method. Common methods are
available in the static class.
An object which can be used to
traverse the tree using the chosen traversal method.
Returns an enumerator that iterates through the tree.
An object that can be used to iterate through the collection.
Tree node with arbitrary number of children.
The class type for the nodes of the tree.
Gets or sets the parent of this node.
Gets or sets the index of this node in the
collection of children nodes of its parent.
Gets or sets the collection of child nodes
under this node.
Gets the next sibling of this node (the node
immediately next to it in its parent's collection).
Gets the previous sibling of this node.
Gets whether this node is a leaf (has no children).
Initializes a new instance of the class.
The index of this node in the children collection of its parent node.
Tree enumeration method delegate.
The class type for the nodes of the tree.
The binary tree to be traversed.
An enumerator traversing the tree.
Tree enumeration method delegate.
The class type for the nodes of the tree.
An enumerator traversing the tree.
Static class with tree traversal methods.
Breadth-first tree traversal method.
Pre-order tree traversal method.
In-order tree traversal method.
Post-order tree traversal method.
Depth-first tree traversal method.
Depth-first tree traversal method.
Red-black tree specialized for key-based value retrieval.
See .
The type of the key.
The type of the value.
Constructs a new using the default
for the key type .
Constructs a new using
the provided implementation.
The element comparer to be used to order elements in the tree.
Constructs a new using the default
for the key type .
Pass true to allow duplicate elements
in the tree; false otherwise.
Constructs a new using
the provided implementation.
The element comparer to be used to order elements in the tree.
Pass true to allow duplicate elements
in the tree; false otherwise.
Vanilla key-based comparer for .
The key type in the key-value pair.
The value type in the key-value pair.
Initializes a new instance of the class.
The comparer to be used to compare keys.
Initializes a new instance of the class.
Compares two objects and returns a value indicating whether
one is less than, equal to, or greater than the other.
The first object to compare.
The second object to compare.
Compares two objects and returns a value indicating whether
one is less than, equal to, or greater than the other.
The first object to compare.
The second object to compare.
Returns a default sort order comparer for the
key-value pair specified by the generic argument.
Two-way dictionary for efficient lookups by both key and value. This
can be used to represent a one-to-one relation among two object types.
The type of right keys in the dictionary.
The type of left keys in the dictionary.
Initializes a new instance of the class
that is empty, has the default initial capacity, and uses the default equality comparer
for the key type.
Initializes a new instance of the class
that is empty, has the specified initial capacity, and uses the default equality comparer
for the key type.
The initial number of elements that this dictionary can contain.
Initializes a new instance of the class
that contains elements copied from the specified dictionary and uses the default equality
comparer for the key type.
The dictionary whose elements are copied to the new .
Gets the reverse dictionary that maps values back to keys.
Gets the number of elements contained in this .
Gets an object that can be used to synchronize access to the .
Gets a value indicating whether access to the is synchronized (thread safe).
Gets a value indicating whether the object has a fixed size.
Gets a value indicating whether the is read-only.
Gets or sets the element with the specified key.
The left key.
Gets or sets the element with the specified key.
The left key.
Gets an containing the keys of the .
Gets an containing the keys of the .
Gets an containing the keys of the .
Gets an containing the values in the .
Gets an containing the values in the .
Gets an containing the values in the .
Returns an enumerator that iterates through the collection.
A that can be used to iterate through the collection.
Returns an enumerator that iterates through a collection.
An object that can be used to iterate through the collection.
Returns an object for the object.
An object for the object.
Adds an element with the provided key and value to the .
The object to use as the key of the element to add.
The object to use as the value of the element to add.
Adds an element with the provided key and value to the object.
The to use as the key of the element to add.
The to use as the value of the element to add.
Adds an item to the .
The object to add to the .
Determines whether the contains an element with the specified key.
The key to locate in the .
true if the contains an element with the key; otherwise, false.
Determines whether the contains a specific value.
The object to locate in the .
true if is found in the ; otherwise, false.
Gets the value associated with the specified key.
The key whose value to get.
When this method returns, the value associated with the specified key, if the key is found; otherwise, the default value for the type of the parameter. This parameter is passed uninitialized.
true if the object that implements contains an element with the specified key; otherwise, false.
Removes the element with the specified key from the .
The key of the element to remove.
true if the element is successfully removed; otherwise, false. This method also returns false if was not found in the original .
Removes the element with the specified key from the object.
The key of the element to remove.
Removes the first occurrence of a specific object from the .
The object to remove from the .
true if was successfully removed from the ; otherwise, false. This method also returns false if is not found in the original .
Determines whether the object contains an element with the specified key.
The key to locate in the object.
true if the contains an element with the key; otherwise, false.
Removes all items from the .
Read-only keyed collection wrapper.
This collection implements a read-only keyed collection. Read-only collections
can not be changed once they are created and are useful for presenting information
to the user without allowing alteration. A keyed collection is a collection whose
elements can be retrieved by key or by index.
The types of the keys in the dictionary.
The type of values in the dictionary.
Initializes a new instance of the
class.
When implemented in a derived class, extracts the key from the specified element.
The element from which to extract the key.
The key for the specified element.
Returns an enumerator that iterates through the collection.
A that can be used to iterate through the collection.
Determines whether the contains an element with the specified key.
The key to locate in the .
true if the contains an element with the key; otherwise, false.
Gets an containing the keys of the .
An containing the keys of the object that implements .
Gets an containing the values in the .
An containing the values in the object that implements .
Gets the value associated with the specified key.
The key whose value to get.
When this method returns, the value associated with the specified key, if the key is found; otherwise, the default value for the type of the parameter. This parameter is passed uninitialized.
true if the object that implements contains an element with the specified key; otherwise, false.
Gets or sets the element with the specified key.
The key.
This collection is read-only
Determines whether the contains a specific value.
The object to locate in the .
true if is found in the ; otherwise, false.
Copies the elements of the ICollection to an Array, starting at a particular Array index.
The one-dimensional Array that is the destination of the elements copied from ICollection. The Array must have zero-based indexing.
The zero-based index in array at which copying begins.
This method is not supported, as this is a read-only collection.
This collection is read-only
This method is not supported, as this is a read-only collection.
This collection is read-only
This method is not supported, as this is a read-only collection.
This collection is read-only
This method is not supported, as this is a read-only collection.
This collection is read-only
This method is not supported, as this is a read-only collection.
This collection is read-only
This method is not supported, as this is a read-only collection.
This collection is read-only
This method is not supported, as this is a read-only collection.
This collection is read-only
Not supported.
This collection is read-only
Returns true.
Sorted dictionary based on a red-black tree.
The type of keys in the collection.
The type of the values in the collection
Creates a new
using the default comparer for the key
type.
Creates a new .
Adds an element with the provided key and value to the .
The object to use as the key of the element to add.
The object to use as the value of the element to add.
Adds an element with the provided key and value to the dictionary.
The key-value pair
containing the desired key and the value to be added.
Removes the element with the specified key from the dictionary.
The key of the element to remove.
true if the element is successfully removed; otherwise, false.
This method also returns false if was not found
in the original dictionary.
Removes the first occurrence of a specific object from the dictionary.
The object to remove from the dictionary.
true if was successfully removed from
the dictionary; otherwise, false. This method also returns false if
is not found in the original dictionary.
Determines whether the dictionary contains an element with the specified key.
The key to locate in the dictionary.
true if the dictionary contains an element with the key; otherwise, false.
Determines whether the dictionary contains a specific value.
The object to locate in the dictionary.
true if is found in the dictionary; otherwise, false.
Gets an
containing the keys of the .
Gets an
containing the values of the .
Gets the value associated with the specified key.
The key whose value to get.
When this method returns, the value associated with the specified key,
if the key is found; otherwise, the default value for the type of the
parameter. This parameter is passed
uninitialized.
true if the dictionary contains an element with the specified key; otherwise, false.
Gets or sets the element with the specified key.
The key.
The requested key was not found in the present tree.
Removes all elements from the dictionary.
Copies the elements of this dictionary to an array, starting at a particular array index.
The one-dimensional Array that is the destination of the elements
copied from ICollection. The array must have zero-based indexing.
The zero-based index in array at which copying begins.
Gets the number of elements on this dictionary.
Gets a value indicating whether this instance is read only.
Returns false.
Returns an enumerator that iterates through the dictionary.
An
object that can be used to iterate through the collection.
Returns an enumerator that iterates through the dictionary.
An object that can be used to iterate through the collection.
Gets the pair with the minimum key stored in the dictionary.
The with
the minimum key present in the dictionary.
Gets the pair with the maximum key stored in the dictionary.
The with
the minimum key present in the dictionary.
Gets the next key-value pair in the dictionary whose key is
the immediate ancestor of the given .
The key whose ancestor must be found.
The key-value pair whose key is the immediate ancestor of .
Gets the next key-value pair in the dictionary whose key is
the immediate ancestor of the given .
The key whose ancestor must be found.
The key-value pair whose key is the immediate ancestor of
, returned as an out parameter.
True if there was an ancestor in the dictionary; false otherwise.
Gets the next key-value pair in the dictionary whose key is
the immediate successor to the given .
The key whose successor must be found.
The key-value pair whose key is the immediate successor of .
Gets the next key-value pair in the dictionary whose key is
the immediate successor to the given .
The key whose successor must be found.
The key-value pair whose key is the immediate sucessor of
, returned as an out parameter.
True if there was a successor in the dictionary; false otherwise.
Possible node colors for s.
Red node.
Black node.
node.
The type of the value to be stored.
Constructs a new empty node.
Constructs a node containing the given .
Gets or sets a reference to this node's parent node.
Gets or sets this node's color.
Gets or sets the value associated with this node.
Returns a that represents this instance.
A that represents this instance.
node.
The type of the key that identifies the value.
The type of the values stored in this node.
Constructs a new empty node.
Constructs a new node containing the given
key and value pair.
Constructs a new node containing the given
key and value pair.
Red-black tree.
A red–black tree is a data structure which is a type of self-balancing binary
search tree. Balance is preserved by painting each node of the tree with one of
two colors (typically called 'red' and 'black') in a way that satisfies certain
properties, which collectively constrain how unbalanced the tree can become in
the worst case. When the tree is modified, the new tree is subsequently rearranged
and repainted to restore the coloring properties. The properties are designed in
such a way that this rearranging and recoloring can be performed efficiently.
The balancing of the tree is not perfect but it is good enough to allow it to
guarantee searching in O(log n) time, where n is the total number of elements
in the tree. The insertion and deletion operations, along with the tree rearrangement
and recoloring, are also performed in O(log n) time.
Tracking the color of each node requires only 1 bit of information per node because
there are only two colors. The tree does not contain any other data specific to its
being a red–black tree so its memory footprint is almost identical to a classic
(uncolored) binary search tree.
References:
-
Steven G. Johnson, The NLopt nonlinear-optimization package,
http://ab-initio.mit.edu/nlopt
-
Wikipedia, The Free Encyclopedia. Red-black tree. Available on:
http://en.wikipedia.org/wiki/Red%E2%80%93black_tree
The type of the value to be stored.
Constructs a new using the
default for type .
Constructs a new using
the provided implementation.
The element comparer to be used to order elements in the tree.
Constructs a new using the
default for type .
Pass true to allow duplicate elements
in the tree; false otherwise.
Constructs a new using
the provided implementation.
The element comparer to be used to order elements in the tree.
Pass true to allow duplicate elements
in the tree; false otherwise.
Gets the number of nodes contained in this red-black tree.
Gets the for this red black tree.
Removes all nodes from the tree.
Adds a new item to the tree. If the element already
belongs to this tree, no new element will be added.
The item to be added.
The node containing the added item.
Adds a new item to the tree. If the element already
belongs to this tree, no new element will be added.
The node to be added to the tree.
Attempts to remove an element from the tree.
The item to be removed.
True if the element was in the tree and was removed; false otherwise.
Removes a node from the tree.
The node to be removed.
True if the element was in the tree and was removed; false otherwise.
Removes a node from the tree.
The key of the node to be removed.
A reference to the removed node, if the item was in the tree; otherwise, null.
Removes a node from the tree.
The node to be removed.
A reference to the removed node.
Copies the nodes of this tree to an array, starting at a
particular array index.
The one-dimensional array that is the destination of the elements
copied from this tree. The array must have zero-based indexing.
The zero-based index in at which copying begins.
Copies the elements of this tree to an array, starting at a
particular array index.
The one-dimensional array that is the destination of the elements
copied from this tree. The array must have zero-based indexing.
The zero-based index in at which copying begins.
Gets a value indicating whether this instance is read only.
In a , this returns false.
Returns false.
Returns an enumerator that iterates through this tree in-order.
An object that can
be used to traverse through this tree using in-order traversal.
Returns an enumerator that iterates through this tree in-order.
An object that can
be used to traverse through this tree using in-order traversal.
Determines whether this tree contains the specified item.
The item to be looked for.
true if the element was found inside the tree; otherwise, false.
Determines whether this tree contains the specified item.
The item to be looked for.
true if the element was found inside the tree; otherwise, false.
Attempts to find a node that contains the specified key.
The key whose node is to be found.
A containing the desired
if it is present in the dictionary; otherwise, returns null.
Finds the greatest point in the subtree rooted at
that is less than or equal to (<=) k. In other words, finds either
k or a number immediately below it.
The subtree where search will take place.
A reference value to be found.
The node containing the given value or
its immediately smaller neighboring number present in the tree.
Finds the greatest point in the
tree that is less than or equal to (<=) k.
In other words, finds either k or a number immediately
below it.
A reference for the value to be found.
The node containing the given value or
its immediately smaller neighboring number present in the tree.
Finds the greatest point in the subtree rooted at
that is less than (<) k. In other words, finds a number stored in
the tree that is immediately below k.
The subtree where search will take place.
A reference value to be found.
The node containing an element that is immediately below .
Finds the greatest point in the
tree that is less than (<) k. In other words, finds
a number stored in the tree that is immediately below k.
A reference value to be found.
The node containing an element that is immediately below .
Finds the smallest point in the subtree rooted at
that is greater than (>) k. In other words, finds a number stored in
the tree that is immediately above k.
The subtree where search will take place.
A reference value to be found.
The node containing an element that is immediately below .
Finds the smallest point in the in the
tree that is greater than (>) k. In other words, finds a
number stored in the tree that is immediately above k.
A reference value to be found.
The node containing an element that is immediately below .
Finds the minimum element stored in the tree.
The that
holds the minimum element in the tree.
Finds the maximum element stored in the tree.
The that
holds the maximum element in the tree.
Gets the node that contains the next in-order value coming
after the value contained in the given .
The current node.
The node that contains a value that is immediately greater than
the current value contained in the given .
Gets the node that contains the previous in-order value coming
before the value contained in the given .
The current node.
The node that contains a value that is immediately less than
the current value contained in the given .
Forces a re-balance of the tree by removing and inserting the same node.
The node to be re-balanced.
The same node, or a new one if it had to be recreated.
Temporary internal framework class for handling debug assertions
while inside unit tests. Will be removed in a future release.
Throws an exception if a condition is false.
Common interface for feature descriptors.
Gets or sets the descriptor vector associated with this point.
Common interface for feature extractors.
An extension of providing
atomic operations such as Add and Increment to floating point numbers.
Adds two 32-bit floating point values and replaces the first
double value with their sum, as an atomic operation.
The first variable to be added.
The second variable to be added.
The updated value of the first variable.
Increments a specified variable and stores the result, as an atomic operation.
The variable to be incremented.
The updated value of the variable.
Common interface for classification models. Classification models
learn how to produce a class-label (or a set of class labels) y
from an input vector x.
Gets or sets the number of classes expected and recognized by the classifier.
Common interface for classification models. Classification models
learn how to produce a class-label (or a set of class labels) y
from an input vector x.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int.
Computes a class-label decision for a given .
The input vector that should be classified into
one of the possible classes.
A class-label that best described according
to this classifier.
Computes class-label decisions for each vector in the given .
The input vectors that should be classified into
one of the possible classes.
The class-labels that best describe each
vectors according to this classifier.
Computes class-label decisions for each vector in the given .
The input vectors that should be classified into
one of the possible classes.
The location where to store the class-labels.
The class-labels that best describe each
vectors according to this classifier.
Common interface for multi-class models. Classification models
learn how to produce a class-label y from an input vector x.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int.
Common interface for multi-class models. Classification models
learn how to produce a class-label y from an input vector x.
The data type for the input data. Default is double[].
Computes a class-label decision for a given .
The input vector that should be classified into
one of the possible classes.
A class-label that best described according
to this classifier.
Computes class-label decisions for each vector in the given .
The input vectors that should be classified into
one of the possible classes.
The class-labels that best describe each
vectors according to this classifier.
Views this instance as a multi-label classifier,
giving access to more advanced methods, such as the prediction
of one-hot vectors.
This instance seen as an .
Common interface for multi-class models. Classification models
learn how to produce a class-label y from an input vector x.
Common interface for score-based multi-class classifiers. A multi-class
classifier can predict to which class an instance belongs based
on a decision score (a real number) that measures the association of the
input with each class.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int.
Predicts a class label for each input vector, returning a
numerical score measuring the strength of association of the
input vector to the most strongly related class.
A set of input vectors.
The class labels predicted for each input
vector, as predicted by the classifier.
Predicts a class label for each input vector, returning a
numerical score measuring the strength of association of the
input vector to the most strongly related class.
A set of input vectors.
The class labels predicted for each input
vector, as predicted by the classifier.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Common interface for score-based multi-class classifiers. A multi-class
classifier can predict to which class an instance belongs based
on a decision score (a real number) that measures the association of the
input with each class.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int.
Predicts a class label for the input vector, returning a
numerical score measuring the strength of association of the
input vector to its most strongly related class.
The input vector.
The class label predicted by the classifier.
Common interface for score-based multi-class classifiers. A multi-class
classifier can predict to which class an instance belongs based
on a decision score (a real number) that measures the association of the
input with each class.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int.
Common interface for score-based multi-class classifiers. A multi-class
classifier can predict to which class an instance belongs based
on a decision score (a real number) that measures the association of the
input with each class.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int.
Common interface for score-based multi-class classifiers. A multi-class
classifier can predict to which class an instance belongs based
on a decision score (a real number) that measures the association of the
input with each class.
The data type for the input data. Default is double[].
Computes a class-label decision for a given .
The input vector that should be classified into
one of the possible classes.
A class-label that best described according
to this classifier.
Computes class-label decisions for each vector in the given .
The input vectors that should be classified into
one of the possible classes.
The class-labels that best describe each
vectors according to this classifier.
Computes a numerical score measuring the association between
the given vector and its most strongly
associated class (as predicted by the classifier).
The input vector.
Computes a numerical score measuring the association between
each of the given vectors and their
respective most strongly associated classes.
A set of input vectors.
Computes a numerical score measuring the association between
each of the given vectors and their
respective most strongly associated classes.
A set of input vectors.
An array where the result will be stored,
avoiding unnecessary memory allocations.
Views this instance as a multi-label distance classifier,
giving access to more advanced methods, such as the prediction
of one-hot vectors.
This instance seen as an .
Common interface for generative multi-class classifiers. A multi-class
classifier can predicts a class label based on an input instance vector.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int.
Predicts a class label for each input vector, returning the
probability that each vector belongs to its predicted class.
A set of input vectors.
The class labels associated with each input
vector, as predicted by the classifier. If passed as null, the classifier
will create a new array.
Predicts a class label for each input vector, returning the
probability that each vector belongs to its predicted class.
A set of input vectors.
The class labels associated with each input
vector, as predicted by the classifier. If passed as null, the classifier
will create a new array.
An array where the probabilities will be stored,
avoiding unnecessary memory allocations.
Predicts a class label for each input vector, returning the
log-likelihood that each vector belongs to its predicted class.
A set of input vectors.
The class labels associated with each input
vector, as predicted by the classifier. If passed as null, the classifier
will create a new array.
Predicts a class label for each input vector, returning the
log-likelihood that each vector belongs to its predicted class.
A set of input vectors.
The class labels associated with each input
vector, as predicted by the classifier. If passed as null, the classifier
will create a new array.
An array where the log-likelihoods will be stored,
avoiding unnecessary memory allocations.
Common interface for generative multi-class classifiers. A multi-class
classifier can predicts a class label based on an input instance vector.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int.
Predicts a class label for the given input vector, returning the
probability that the input vector belongs to its predicted class.
The input vector.
The class label predicted by the classifier.
Predicts a class label vector for the given input vector, returning the
log-likelihood that the input vector belongs to its predicted class.
The input vector.
The class label predicted by the classifier.
Common interface for generative multi-class classifiers. A multi-class
classifier can predicts a class label based on an input instance vector.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int.
Common interface for generative multi-class classifiers. A multi-class
classifier can predicts a class label based on an input instance vector.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int.
Common interface for generative multi-class classifiers. A multi-class
classifier can predicts a class label based on an input instance vector.
The data type for the input data. Default is double[].
Computes a class-label decision for a given .
The input vector that should be classified into
one of the possible classes.
A class-label that best described according
to this classifier.
Computes class-label decisions for each vector in the given .
The input vectors that should be classified into
one of the possible classes.
The class-labels that best describe each
vectors according to this classifier.
Predicts a class label for the given input vector, returning the
probability that the input vector belongs to its predicted class.
The input vector.
Predicts a class label for the given input vectors, returning the
probability that the input vector belongs to its predicted class.
The input vector.
Predicts a class label for the given input vectors, returning the
probability that the input vector belongs to its predicted class.
The input vector.
An array where the probabilities will be stored,
avoiding unnecessary memory allocations.
Predicts a class label vector for the given input vector, returning the
log-likelihood that the input vector belongs to its predicted class.
The input vector.
Predicts a class label vector for the given input vectors, returning the
log-likelihood that the input vector belongs to its predicted class.
The input vector.
Predicts a class label vector for the given input vectors, returning the
log-likelihood that the input vector belongs to its predicted class.
The input vector.
An array where the log-likelihoods will be stored,
avoiding unnecessary memory allocations.
Views this instance as a multi-label generative classifier,
giving access to more advanced methods, such as the prediction
of one-hot vectors.
This instance seen as an .
Common interface for multi-label classifiers. A multi-label classifier can
predict the occurrence of multiple class labels at once.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int[].
Computes class-label decisions for the given .
The input vectors that should be classified as
any of the possible classes.
The location where to store the class-labels.
A set of class-labels that best describe the
vectors according to this classifier.
Common interface for multi-label classifiers. A multi-label classifier can
predict the occurrence of multiple class labels at once.
The data type for the input data. Default is double[].
Common interface for multi-label classifiers. A multi-label classifier can
predict the occurrence of multiple class labels at once.
Common interface for score-based multi-label classifiers. A multi-label
classifier can predict the occurrence of multiple class labels at once
based on a decision score (a real number) computed for each class.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int[].
Predicts a class label vector for each input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
A set of input vectors.
The class labels associated with each input
vector, as predicted by the classifier. If passed as null, the classifier
will create a new array.
Predicts a class label vector for each input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
A set of input vectors.
The class labels associated with each input
vector, as predicted by the classifier. If passed as null, the classifier
will create a new array.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Common interface for score-based multi-label classifiers. A multi-label
classifier can predict the occurrence of multiple class labels at once
based on a decision score (a real number) computed for each class.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int[].
Predicts a class label vector for the given input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
A set of input vectors.
The class labels associated with each input
vector, as predicted by the classifier. If passed as null, the classifier
will create a new array.
Predicts a class label vector for the given input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
A set of input vectors.
The class labels associated with each input
vector, as predicted by the classifier. If passed as null, the classifier
will create a new array.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Common interface for score-based multi-label classifiers. A multi-label
classifier can predict the occurrence of multiple class labels at once
based on a decision score (a real number) computed for each class.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int[].
Predicts a class label vector for the given input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
The input vector.
The class label predicted by the classifier.
Predicts a class label vector for the given input vector, returning a
numerical score measuring the strength of association of the input vector
to each of the possible classes.
A set of input vectors.
The class label predicted by the classifier.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Common interface for score-based multi-label classifiers. A multi-label
classifier can predict the occurrence of multiple class labels at once
based on a decision score (a real number) computed for each class.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int[].
Common interface for score-based multi-label classifiers. A multi-label
classifier can predict the occurrence of multiple class labels at once
based on a decision score (a real number) computed for each class.
The data type for the input data. Default is double[].
Computes a numerical score measuring the association between
the given vector and a given
.
The input vector.
The index of the class whose score will be computed.
Computes a numerical score measuring the association between
each of the given vectors and the
given class indices.
A set of input vectors.
The class indices associated with each
input vector whose scores will be computed.
Computes a numerical score measuring the association between
each of the given vectors and the
given class indices.
A set of input vectors.
The class indices associated with each
input vector whose scores will be computed.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Computes a numerical score measuring the association between
each of the given vectors and the
given class indices.
A set of input vectors.
The index of the class whose score will be computed.
Computes a numerical score measuring the association between
each of the given vectors and the
given class indices.
A set of input vectors.
The index of the class whose score will be computed.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Computes a numerical score measuring the association between
the given vector and each class.
The input vector.
Computes a numerical score measuring the association between
the given vector and each class.
The input vector.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Computes a numerical score measuring the association between
each of the given vectors and each
possible class.
A set of input vectors.
Computes a numerical score measuring the association between
each of the given vectors and each
possible class.
A set of input vectors.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Views this instance as a multi-class score-based classifier.
This instance seen as an .
Views this instance as a multi-class score-based classifier.
This instance seen as an .
Common interface for generative multi-label classifiers. A multi-label
classifier can predict the occurrence of multiple class labels at once.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int[].
Predicts a class label vector for each input vector, returning the
probabilities of the input vector belonging to each possible class.
A set of input vectors.
The labels predicted by the classifier.
Predicts a class label vector for each input vector, returning the
probabilities of the input vector belonging to each possible class.
A set of input vectors.
The labels predicted by the classifier.
An array where the probabilities will be stored,
avoiding unnecessary memory allocations.
Predicts a class label vector for each input vector, returning the
log-likelihoods of the input vector belonging to each possible class.
A set of input vectors.
The labels predicted by the classifier.
Predicts a class label vector for each input vector, returning the
log-likelihoods of the input vector belonging to each possible class.
A set of input vectors.
The labels predicted by the classifier.
An array where the log-likelihoods will be stored,
avoiding unnecessary memory allocations.
Common interface for generative multi-label classifiers. A multi-label
classifier can predict the occurrence of multiple class labels at once,
as well as their probabilities.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int[].
Predicts a class label vector for the given input vector, returning the
probabilities of the input vector belonging to each possible class.
A set of input vectors.
The class labels associated with each input
vector, as predicted by the classifier. If passed as null, the classifier
will create a new array.
Predicts a class label vector for the given input vector, returning the
probabilities of the input vector belonging to each possible class.
A set of input vectors.
The class labels associated with each input
vector, as predicted by the classifier. If passed as null, the classifier
will create a new array.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Predicts a class label vector for the given input vector, returning the
log-likelihoods of the input vector belonging to each possible class.
A set of input vectors.
The class labels associated with each input
vector, as predicted by the classifier. If passed as null, the classifier
will create a new array.
Predicts a class label vector for the given input vector, returning the
log-likelihoods of the input vector belonging to each possible class.
A set of input vectors.
The class labels associated with each input
vector, as predicted by the classifier. If passed as null, the classifier
will create a new array.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Common interface for generative multi-label classifiers. A multi-label
classifier can predict the occurrence of multiple class labels at once,
as well as their probabilities.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int[].
Predicts a class label vector for the given input vector, returning the
probabilities of the input vector belonging to each possible class.
The input vector.
The class label predicted by the classifier.
Predicts a class label vector for the given input vector, returning the
probabilities of the input vector belonging to each possible class.
The input vector.
The class label predicted by the classifier.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Predicts a class label vector for the given input vector, returning the
log-likelihoods of the input vector belonging to each possible class.
The input vector.
The class label predicted by the classifier.
Predicts a class label vector for the given input vector, returning the
log-likelihoods of the input vector belonging to each possible class.
The input vector.
The class label predicted by the classifier.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Common interface for generative multi-label classifiers. A multi-label
classifier can predict the occurrence of multiple class labels at once.
The data type for the input data. Default is double[].
The data type for the class labels. Default is int[].
Common interface for generative multi-label classifiers. A multi-label
classifier can predict the occurrence of multiple class labels at once,
as well as their probabilities.
The data type for the input data. Default is double[].
Computes the probability that the given input vector
belongs to the specified .
The input vector.
The index of the class whose score will be computed.
Computes the probability that the given input vectors
belongs to each class specified in .
A set of input vectors.
The class indices associated with each
input vector whose scores will be computed.
Computes the probability that the given input vectors
belongs to each class specified in .
A set of input vectors.
The class indices associated with each
input vector whose scores will be computed.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Computes the probability that the given input vectors
belongs to each class specified in .
A set of input vectors.
The class indices associated with each
input vector whose scores will be computed.
Computes the probability that the given input vectors
belongs to each class specified in .
A set of input vectors.
The class indices associated with each
input vector whose scores will be computed.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Computes the log-likelihood that the given input vector
belongs to the specified .
The input vector.
The index of the class whose score will be computed.
Computes the log-likelihood that the given input vectors
belongs to each class specified in .
A set of input vectors.
The class indices associated with each
input vector whose scores will be computed.
Computes the log-likelihood that the given input vectors
belongs to each class specified in .
A set of input vectors.
The class indices associated with each
input vector whose scores will be computed.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Computes the log-likelihood that the given input vectors
belongs to each class specified in .
A set of input vectors.
The class indices associated with each
input vector whose scores will be computed.
Computes the log-likelihood that the given input vectors
belongs to each class specified in .
A set of input vectors.
The class indices associated with each
input vector whose scores will be computed.
An array where the distances will be stored,
avoiding unnecessary memory allocations.
Computes the log-likelihood that the given input
vector belongs to each of the possible classes.
The input vector.
Computes the log-likelihood that the given input
vector belongs to each of the possible classes.
The input vector.
An array where the log-likelihoods will be stored,
avoiding unnecessary memory allocations.
Computes the log-likelihoods that the given input
vectors belongs to each of the possible classes.
A set of input vectors.
Computes the log-likelihoods that the given input
vectors belongs to each of the possible classes.
A set of input vectors.
An array where the log-likelihoods will be stored,
avoiding unnecessary memory allocations.
Computes the probabilities that the given input
vector belongs to each of the possible classes.
The input vector.
Computes the probabilities that the given input
vector belongs to each of the possible classes.
The input vector.
An array where the probabilities will be stored,
avoiding unnecessary memory allocations.
Computes the probabilities that the given input
vectors belongs to each of the possible classes.
A set of input vectors.
Computes the probabilities that the given input
vectors belongs to each of the possible classes.
A set of input vectors.
An array where the probabilities will be stored,
avoiding unnecessary memory allocations.
Views this instance as a multi-class generative classifier.
This instance seen as an .
Views this instance as a multi-class generative classifier.
This instance seen as an .
Common interface for classification models. Classification models
learn how to produce a class-label (or a set of class labels) y
from an input vector x.
The data type for the input data. Default is double[].
Views this instance as a multi-class classifier,
giving access to more advanced methods, such as the prediction
of integer labels.
This instance seen as an .
Views this instance as a multi-class classifier,
giving access to more advanced methods, such as the prediction
of integer labels.
This instance seen as an .
Common interface for classification models. Classification models
learn how to produce a class-label (or a set of class labels) y
from an input vector x.
Common interface for score-based binary classifiers. A binary classifier can
predict whether or not an instance belongs to a class based on a decision score
(a real number) that measures the association of the input with the negative
and positive class.
The data type for the input data. Default is double[].
Views this instance as a multi-class distance classifier,
giving access to more advanced methods, such as the prediction
of integer labels.
This instance seen as an .
Views this instance as a multi-class distance classifier,
giving access to more advanced methods, such as the prediction
of integer labels.
This instance seen as an .
Common interface for generative binary classifiers. A binary classifier can
predict whether or not an instance belongs to a class, while at the same time
being able to provide the probability of this sample belonging to the positive
class.
The data type for the input data. Default is double[].
Views this instance as a multi-class generative classifier,
giving access to more advanced methods, such as the prediction
of integer labels.
This instance seen as an .
Views this instance as a multi-class generative classifier,
giving access to more advanced methods, such as the prediction
of integer labels.
This instance seen as an .
Common interface for generative observation sequence taggers. A sequence
tagger can predict the class label of each individual observation in a
input sequence vector.
The data type for the input data. Default is double[].
Common interface for generative observation sequence taggers. A sequence
tagger can predict the class label of each individual observation in a
input sequence vector.
The data type for the input data. Default is double[].
Predicts a the probability that the sequence vector
has been generated by this log-likelihood tagger.
Predicts a the probability that the sequence vector
has been generated by this log-likelihood tagger.
Predicts a the probability that the sequence vector
has been generated by this log-likelihood tagger.
Predicts a the probability that the sequence vector
has been generated by this log-likelihood tagger.
Predicts a the probability that the sequence vector
has been generated by this log-likelihood tagger.
Predicts a the probability that the sequence vector
has been generated by this log-likelihood tagger.
Predicts a the probability that the sequence vector
has been generated by this log-likelihood tagger.
Predicts a the log-likelihood that the sequence vector
has been generated by this log-likelihood tagger.
Predicts a the log-likelihood that the sequence vector
has been generated by this log-likelihood tagger.
Predicts a the log-likelihood that the sequence vector
has been generated by this log-likelihood tagger.
Predicts a the log-likelihood that the sequence vector
has been generated by this log-likelihood tagger.
Predicts a the log-likelihood that the sequence vector
has been generated by this log-likelihood tagger.
Predicts a the probabilities for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the probabilities for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the probabilities for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the probabilities for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the log-likelihood for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the log-likelihood for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the log-likelihood for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the log-likelihood for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the probabilities for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the probabilities for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the probabilities for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the probabilities for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the log-likelihood for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the log-likelihood for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the log-likelihood for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Predicts a the log-likelihood for each of the observations in
the sequence vector assuming each of the possible states in the
tagger model.
Common interface for observation sequence taggers.
Computes numerical scores measuring the association between
each of the given vectors and each
possible class.
Computes numerical scores measuring the association between
each of the given vectors and each
possible class.
Computes numerical scores measuring the association between
each of the given vectors and each
possible class.
Computes numerical scores measuring the association between
each of the given vectors and each
possible class.
Computes numerical scores measuring the association between
each of the given vectors and each
possible class.
Computes numerical scores measuring the association between
each of the given vectors and each
possible class.
Computes numerical scores measuring the association between
each of the given vectors and each
possible class.
Computes numerical scores measuring the association between
each of the given vectors and each
possible class.
Common interface for parallel algorithms.
Gets or sets the parallelization options for this algorithm. It can
be used to control the maximum number of cores that should be used
during the algorithm's execution.
The interface is implemented by most machine learning
algorithms in the framework, and it is most common use is to allow the user to
tune how many cores should be used by a multi-threaded learning algorithm.
In the following example, we will be using the property to limit
the maximum degree of parallelism of a support vector machine learning algorithm to be 1, meaning
the algorithm will be running in a single thread:
if (r1 + Rational.Epsilon > r2 && r1 - Rational.Epsilon < r2)
{
// Difference between r1 and r2 is less than Rational.Epsilon.
}
Converts the string representation of a number to its representation.
A string that represents a number.
The parsed value.
Converts the string representation of a number to its representation.
A string that represents a number.
Indicates the styles that can be present when parsing a number.
The parsed value.
Converts the string representation of a number to its representation.
A string that represents a number.
An object that supplies culture-specific information about the format of .
The parsed value.
Converts the string representation of a number to its representation.
A string that represents a number.
Indicates the styles that can be present when parsing a number.
An object that supplies culture-specific information about the format of .
The parsed value.
Converts the string representation of a number to its representation. A return value indicates whether the conversion succeeded or failed.
A string that represents a number.
When this method returns, contains the parsed value.
True if the conversion succeeded; otherwise false.
Converts the string representation of a number to its representation. A return value indicates whether the conversion succeeded or failed.
A string that represents a number.
Indicates the styles that can be present when parsing a number.
When this method returns, contains the parsed value.
True if the conversion succeeded; otherwise false.
Converts the string representation of a number to its representation. A return value indicates whether the conversion succeeded or failed.
A string that represents a number.
An object that supplies culture-specific information about the format of .
When this method returns, contains the parsed value.
True if the conversion succeeded; otherwise false.
Converts the string representation of a number to its representation. A return value indicates whether the conversion succeeded or failed.
A string that represents a number.
Indicates the styles that can be present when parsing a number.
An object that supplies culture-specific information about the format of .
When this method returns, contains the parsed value.
True if the conversion succeeded; otherwise false.
Converts a floating-point number to a rational number.
A floating-point number to convert to a rational number.
The maximum error allowed between the result and the input value.
A rational number.
Converts a floating-point number to a rational number.
A floating-point number to convert to a rational number.
The maximum error allowed between the result and the input value.
A rational number.
Converts a floating-point decimal to a rational number.
A floating-point number to convert to a rational number.
The maximum value that the denominator can have.
The desired maximum error between the result and the input value. This is only used as an alternative end condition;
the actual error may be greater because of the limitation on the denominator value.
A rational number.
Returns the absolute value of a Rational.
A Rational number.
Gets the absolute value of the Rational.
Returns the smaller of two Rationals.
A Rational number.
A Rational number.
The smaller of two Rationals.
Returns the larger of two Rationals.
A Rational number.
A Rational number.
The larger of two Rationals.
Rounds a to the nearest integral value.
The number to round.
The rounded number.
The denominator of is zero.
Returns a specified number raised to a specified power.
A Rational number.
A Rational number.
The larger of two Rationals.
Calculates the quotient of two rational numbers and also returns the remainder in an output parameter.
The dividend.
The divisor.
The remainder.
The quotient.
Returns a value that indicates whether the specified evaluates to positive or negative infinity.
A rational number.
True if evaluates to positive or negative infinity; otherwise false.
Returns a value that indicates whether the specified evaluates to positive infinity.
A rational number.
True if evaluates to positive infinity; otherwise false.
Returns a value that indicates whether the specified evaluates to negative infinity.
A rational number.
True if evaluates to negative infinity; otherwise false.
Returns a value that indicates whether the specified represents an indeterminate value.
A rational number.
True if represents an indeterminate value; otherwise false.
Returns a value that indicates whether the specified evaluates to zero.
A rational number.
True if evaluates to zero; otherwise false.
Creates a new instance with a denominator of 1.
The numerator of the .
Creates a new instance.
The numerator of the .
The denominator of the .
Creates a new instance equal to a floating-point value.
A floating-point value
Gets the numerator of the current value.
Gets the numerator of the current value.
Gets the floating-point equivalent of the current value.
Gets the inverse of the current value (that is, one divided by the current value).
Gets the negated version of the current value (that is, the current value multiplied by negative one).
Gets the simplified version of the rational number.
Gets the simplified version of the rational number.
Gets the simplified version of the rational number.
Converts the current to a double.
A .
Converts the Rational to a string in the form of an improper fraction.
A string representaion of the rational number.
Converts this instance to its equivalent string representation.
An object that has culture-specific formatting information.
The string representation of the .
Converts the Rational to a string in the form of a mixed fraction.
A string representaion of the rational number.
Converts the Rational to a string in the form of a mixed fraction.
The separator between the number part and the fraction part
A string representaion of the rational number.
Indicates whether this instance and a specified object are equal.
Another object to compare to.
true if the current object and are the same type and represent the same value; otherwise, false.
Indicates whether this instance and a specified are strictly equal; that is, the two instances must have equal numerators and denominators.
Another to compare to.
true if the current number and have equal numerators and denominators; otherwise, false.
The basic Equals implementation considers unsimplified fractions to be equal to their simplified forms; e.g. 2/4 = 1/2.
This method considers those values to be different.
Returns the hash code for this instance.
the hash code.
Gets whether two values are numerically equivalent.
A .
A .
True if the values are equivalent; otherwise false.
Gets whether two values are numerically not equivalent.
A .
A .
False if the values are equivalent; otherwise true.
Gets whether a values is greater than another.
A .
A .
True if is greater than ; otherwise false.
Gets whether a values is greater than or equal to another.
A .
A .
True if is greater than or equal to ; otherwise false.
Gets whether a values is less than another.
A .
A .
True if is less than ; otherwise false.
Gets whether a values is less than or equal to another.
A .
A .
True if is less than or equal to ; otherwise false.
Gets the value.
A .
A .
Gets the negated version of a value.
A .
A .
Adds two values.
A .
A .
The simplified sum of the values.
Subtracts two values.
A .
A .
The simplified difference of the values.
Multiplies two values.
A .
A .
The simplified product of the values.
Divides two values.
A .
A .
The simplified dividend of the values.
Gets the remainder that results from dividing two values.
A .
A .
The remainder that results from dividing the values.
Increments a value.
A .
The incremented .
Decrements a value.
A .
The idecremented .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Converts the specified to a .
The to convert.
A .
Compares this instance to another and returns an indication of their relative values.
The value to compare to. This should be a numeric value.
A signed number indicating the relative values of this instance and .
Compares this instance to another and returns an indication of their relative values.
The to compare to.
A signed number indicating the relative values of this instance and .
Converts this instance to its equivalent string representation.
The format to use for both the numerator and the denominator.
An object that has culture-specific formatting information.
The string representation of the .
Indicates whether this instance and a specified are equal.
Another to compare to.
true if the current object and represent the same value; otherwise, false.
Hyperrectangle structure.
In geometry, an n-orthotope (also called a hyperrectangle or a box) is the generalization of a rectangle for higher
dimensions, formally defined as the Cartesian product of intervals.
References:
-
Wikipedia contributors, "Hyperrectangle," Wikipedia, The Free Encyclopedia,
https://en.wikipedia.org/w/index.php?title=Hyperrectangle
Gets the minimum point defining the lower bound of the hyperrectangle.
Gets the maximum point defining the upper bound of the hyperrectangle.
Gets the number of dimensions of the hyperrectangle.
Gets the length of each dimension. The length of the first dimension
can be referred as the width, the second as the height, and so on.
Initializes a new instance of the struct.
The x-coordinate of the upper-left corner of the rectangle..
The y-coordinate of the upper-left corner of the rectangle.
The width of the rectangle.
The height of the rectangle.
Initializes a new instance of the struct.
The minimum point in the hyperrectangle (the lower bound).
The maximum point in the hyperrectangle (the upper bound).
Whether the passed vectors should be copied into this instance
or used as-is. Default is true (elements from the given vectors will be copied
into new array instances).
Initializes a new instance of the struct from minimum and maximum values.
The minimum point in the hyperrectangle (the lower bound).
The maximum point in the hyperrectangle (the upper bound).
Whether the passed vectors should be copied into this instance
or used as-is. Default is true (elements from the given vectors will be copied
into new array instances).
Initializes a new instance of the struct from a minimum value and a size.
The minimum point in the hyperrectangle (the lower bound).
The size of each dimension (i.e. width, height, and so on).
Whether the passed vectors should be copied into this instance
or used as-is. Default is true (elements from the given vectors will be copied
into new array instances).
Determines if this rectangle intersects with rect.
Determines if the specified point is contained within this Hyperrectangle structure.
Indicates whether the current object is equal to another object of the same type.
An object to compare with this object.
true if the current object is equal to the parameter; otherwise, false.
Creates a new object that is a copy of the current instance.
A new object that is a copy of this instance.
Returns a that represents this instance.
A that represents this instance.
Returns a that represents this instance.
The format to use.-or- A null reference (Nothing in Visual Basic) to use the default format defined for the type of the implementation.
The provider to use to format the value.-or- A null reference (Nothing in Visual Basic) to obtain the numeric format information from the current locale setting of the operating system.
A that represents this instance.
Extension methods for sparse vectors.
Parses a string containing a sparse array in LibSVM
format into a vector.
An array of "index:value" strings indicating
where each value belongs in the sparse vector.
Whether an intercept term should be added
at the beginning of the vector.
Parses a string containing a sparse array in LibSVM
format into a vector.
An array of "index:value" strings indicating
where each value belongs in the sparse vector.
Whether an intercept term should be added
at the beginning of the vector.
Converts an array of sparse vectors into a jagged matrix.
Converts an array of sparse vectors into a jagged matrix.
Creates a sparse vector from a dense array.
Creates sparse vectors from dense arrays.
Gets the maximum number of columns (dimensions)
that can be inferred from the given sparse vectors.
Creates a sparse vector from a dictionary mapping indices to values.
Sparse vector representation (in LibSVM format).
The type for the non-zero elements in this vector.
Gets or sets the vector of indices indicating the location
of the non-zero elements contained in this sparse vector.
Gets or sets the vector of values indicating which non-zero
value happens at each position indicated in .
Creates a sparse vector with zero elements.
Creates a sparse vector with the maximum number of elements.
The maximum number of non-zero
elements that this vector can accomodate.
Creates a sparse vector from a vector of indices
and a vector of values occuring at those indices.
The indices for non-zero entries.
The non-zero values happening at each index.
Converts this sparse vector to a dense vector of the given length.
Converts this sparse vector to a dense vector of the given length.
Converts this sparse vector to a sparse representation where
the indices are intertwined with their corresponding values.
Gets the the value stored at position .
Creates a new object that is a copy of the current instance.
A new object that is a copy of this instance.
Performs an implicit conversion from to .
Gets the maximum non-zero element index in the sparse vector.
Copies the elements of the to an , starting at a particular index.
The one-dimensional that is the destination of the elements copied from . The must have zero-based indexing.
The zero-based index in at which copying begins.
Returns a that represents this instance.
A that represents this instance.
Returns a that represents this instance.
The format.
The format provider.
A that represents this instance.
Determines whether this Sparse vector has elements on all indices.
true if this instance is full; otherwise, false.
A strongly-typed resource class, for looking up localized strings, etc.
Returns the cached ResourceManager instance used by this class.
Overrides the current thread's CurrentUICulture property for all
resource lookups using this strongly typed resource class.
Looks up a localized string similar to This algorithm does not support instance weights at this time. Please feel free to report this as an enhancement request in the project issue tracker - or even submit a pull request implementing this feature!.
Represents a byte range with minimum and maximum values.
This class represents a byt range with inclusive limits, where
both minimum and maximum values of the range are included into it.
Mathematical notation of such range is [min, max].
// create [0, 255] range
var range1 = new ByteRange(0, 255);
// create [64, 128] range
var range2 = new ByteRange(64, 128);
// check if values is inside of the first range
if (range1.IsInside(5))
{
// ...
}
// check if the second range is inside of the first range
if (range1.IsInside(range2))
{
// ...
}
// check if two ranges overlap
if (range1.IsOverlapping(range2))
{
// ...
}
Minimum value of the range.
Represents minimum value (left side limit) of the range [min, max].
Maximum value of the range.
Represents maximum value (right side limit) of the range [min, max].
Gets the length of the range, defined as (max - min).
Initializes a new instance of the class.
Minimum value of the range.
Maximum value of the range.
Check if the specified value is inside of the range.
Value to check.
True if the specified value is inside of the range or false otherwise.
Check if the specified range is inside of the range.
Range to check.
True if the specified range is inside of the range or false otherwise.
Check if the specified range overlaps with the range.
Range to check for overlapping.
True if the specified range overlaps with the range or false otherwise.
Computes the intersection between two ranges.
The second range for which the intersection should be calculated.
An new structure containing the intersection
between this range and the given as argument.
Determines whether two instances are equal.
Determines whether two instances are not equal.
Indicates whether the current object is equal to another object of the same type.
An object to compare with this object.
true if the current object is equal to the parameter; otherwise, false.
Determines whether the specified , is equal to this instance.
The to compare with this instance.
true if the specified is equal to this instance; otherwise, false.
Returns a hash code for this instance.
A hash code for this instance, suitable for use in hashing algorithms and data structures like a hash table.
Returns a that represents this instance.
A that represents this instance.
Returns a that represents this instance.
The format.
The format provider.
A that represents this instance.
Performs an implicit conversion from to .
The range.
The result of the conversion.
Performs an implicit conversion from to .
The range.
The result of the conversion.
Performs an implicit conversion from to .
The range.
The result of the conversion.
Returns an enumerator that iterates through a collection.
An object that can be used to iterate through the collection.
Returns an enumerator that iterates through a collection.
An object that can be used to iterate through the collection.
Represents a double range with minimum and maximum values.
This class represents a double range with inclusive limits, where
both minimum and maximum values of the range are included into it.
Mathematical notation of such range is [min, max].
// create [0.25, 1.5] range
var range1 = new DoubleRange(0.25, 1.5);
// create [1.00, 2.25] range
var range2 = new DoubleRange(1.00, 2.25);
// check if values is inside of the first range
if (range1.IsInside(0.75))
{
// ...
}
// check if the second range is inside of the first range
if (range1.IsInside(range2))
{
// ...
}
// check if two ranges overlap
if (range1.IsOverlapping(range2))
{
// ...
}
Minimum value of the range.
Represents minimum value (left side limit) of the range [min, max].
Maximum value of the range.
Represents maximum value (right side limit) of the range [min, max].
Gets the length of the range, defined as (max - min).
Initializes a new instance of the class.
Minimum value of the range.
Maximum value of the range.
Check if the specified value is inside of the range.
Value to check.
True if the specified value is inside of the range or false otherwise.
Check if the specified range is inside of the range.
Range to check.
True if the specified range is inside of the range or false otherwise.
Check if the specified range overlaps with the range.
Range to check for overlapping.
True if the specified range overlaps with the range or false otherwise.
Computes the intersection between two ranges.
The second range for which the intersection should be calculated.
An new structure containing the intersection
between this range and the given as argument.
Determines whether two instances are equal.
Determines whether two instances are not equal.
Indicates whether the current object is equal to another object of the same type.
An object to compare with this object.
true if the current object is equal to the parameter; otherwise, false.
Determines whether the specified , is equal to this instance.
The to compare with this instance.
true if the specified is equal to this instance; otherwise, false.
Returns a hash code for this instance.
A hash code for this instance, suitable for use in hashing algorithms and data structures like a hash table.
Returns a that represents this instance.
A that represents this instance.
Returns a that represents this instance.
The format.
The format provider.
A that represents this instance.
Converts this double-precision range into an .
Specifies if inner integer range must be returned or outer range.
Returns integer version of the range.
If is set to , then the
returned integer range will always fit inside of the current single precision range.
If it is set to , then current single precision range will always
fit into the returned integer range.
Event arguments holding a buffer sent or received during some communication process.
Length of the transfered message.
Initializes a new instance of the class.
Message being transfered during communication process.
Initializes a new instance of the class.
Buffer containing the message being transferred during communication process.
Starting index of the message within the buffer.
Length of the message within the buffer.
Get the transfered message.
Returns copy of the transfered message.
Get the transferred message as string.
Returns string encoding the transferred message.
Connection failed exception.
The exception is thrown in the case if connection to device
has failed.
Initializes a new instance of the class.
Initializes a new instance of the class.
Exception's message.
Initializes a new instance of the class.
The error message that explains the reason for the exception.
The exception that is the cause of the current exception, or a null reference (Nothing in Visual Basic) if no inner exception is specified.
Connection lost exception.
The exception is thrown in the case if connection to device
is lost. When the exception is caught, user may need to reconnect to the device.
Initializes a new instance of the class.
Initializes a new instance of the class.
Exception's message.
Initializes a new instance of the class.
The error message that explains the reason for the exception.
The exception that is the cause of the current exception, or a null reference (Nothing in Visual Basic) if no inner exception is specified.
Initializes a new instance of the class.
The that holds the serialized object data about the exception being thrown.
The that contains contextual information about the source or destination.
Not connected exception.
The exception is thrown in the case if connection to device
is not established, but user requests for its services.
Initializes a new instance of the class.
Initializes a new instance of the class.
Exception's message.
Initializes a new instance of the class.
The error message that explains the reason for the exception.
The exception that is the cause of the current exception, or a null reference (Nothing in Visual Basic) if no inner exception is specified.
Initializes a new instance of the class.
The that holds the serialized object data about the exception being thrown.
The that contains contextual information about the source or destination.
Device busy exception.
The exception is thrown in the case if access to certain device
is not available due to the fact that it is currently busy handling other request/connection.
Initializes a new instance of the class.
Initializes a new instance of the class.
Exception's message.
Initializes a new instance of the class.
The error message that explains the reason for the exception.
The exception that is the cause of the current exception, or a null reference (Nothing in Visual Basic) if no inner exception is specified.
Initializes a new instance of the class.
The that holds the serialized object data about the exception being thrown.
The that contains contextual information about the source or destination.
Device error exception.
The exception is thrown in the case if some error happens with a device, which
may need to be reported to user.
Initializes a new instance of the class.
Initializes a new instance of the class.
Exception's message.
Initializes a new instance of the class.
The error message that explains the reason for the exception.
The exception that is the cause of the current exception, or a null reference (Nothing in Visual Basic) if no inner exception is specified.
Initializes a new instance of the class.
The that holds the serialized object data about the exception being thrown.
The that contains contextual information about the source or destination.
Structure for representing a pair of coordinates of integer type.
The structure is used to store a pair of integer coordinates.
Sample usage:
// assigning coordinates in the constructor
IntPoint p1 = new IntPoint( 10, 20 );
// creating a point and assigning coordinates later
IntPoint p2;
p2.X = 30;
p2.Y = 40;
// calculating distance between two points
float distance = p1.DistanceTo( p2 );
X coordinate.
Y coordinate.
Initializes a new instance of the structure.
X axis coordinate.
Y axis coordinate.
Calculate Euclidean distance between two points.
Point to calculate distance to.
Returns Euclidean distance between this point and
points.
Calculate squared Euclidean distance between two points.
Point to calculate distance to.
Returns squared Euclidean distance between this point and
points.
Addition operator - adds values of two points.
First point for addition.
Second point for addition.
Returns new point which coordinates equal to sum of corresponding
coordinates of specified points.
Addition operator - adds values of two points.
First point for addition.
Second point for addition.
Returns new point which coordinates equal to sum of corresponding
coordinates of specified points.
Subtraction operator - subtracts values of two points.
Point to subtract from.
Point to subtract.
Returns new point which coordinates equal to difference of corresponding
coordinates of specified points.
Subtraction operator - subtracts values of two points.
Point to subtract from.
Point to subtract.
Returns new point which coordinates equal to difference of corresponding
coordinates of specified points.
Addition operator - adds scalar to the specified point.
Point to increase coordinates of.
Value to add to coordinates of the specified point.
Returns new point which coordinates equal to coordinates of
the specified point increased by specified value.
Addition operator - adds scalar to the specified point.
Point to increase coordinates of.
Value to add to coordinates of the specified point.
Returns new point which coordinates equal to coordinates of
the specified point increased by specified value.
Subtraction operator - subtracts scalar from the specified point.
Point to decrease coordinates of.
Value to subtract from coordinates of the specified point.
Returns new point which coordinates equal to coordinates of
the specified point decreased by specified value.
Subtraction operator - subtracts scalar from the specified point.
Point to decrease coordinates of.
Value to subtract from coordinates of the specified point.
Returns new point which coordinates equal to coordinates of
the specified point decreased by specified value.
Multiplication operator - multiplies coordinates of the specified point by scalar value.
Point to multiply coordinates of.
Multiplication factor.
Returns new point which coordinates equal to coordinates of
the specified point multiplied by specified value.
Multiplication operator - multiplies coordinates of the specified point by scalar value.
Point to multiply coordinates of.
Multiplication factor.
Returns new point which coordinates equal to coordinates of
the specified point multiplied by specified value.
Division operator - divides coordinates of the specified point by scalar value.
Point to divide coordinates of.
Division factor.
Returns new point which coordinates equal to coordinates of
the specified point divided by specified value.
Division operator - divides coordinates of the specified point by scalar value.
Point to divide coordinates of.
Division factor.
Returns new point which coordinates equal to coordinates of
the specified point divided by specified value.
Equality operator - checks if two points have equal coordinates.
First point to check.
Second point to check.
Returns if coordinates of specified
points are equal.
Inequality operator - checks if two points have different coordinates.
First point to check.
Second point to check.
Returns if coordinates of specified
points are not equal.
Check if this instance of equal to the specified one.
Another point to check equalty to.
Return if objects are equal.
Get hash code for this instance.
Returns the hash code for this instance.
Implicit conversion to .
Integer point to convert to single precision point.
Returns new single precision point which coordinates are implicitly converted
to floats from coordinates of the specified integer point.
Implicit conversion to .
Integer point to convert to double precision point.
Returns new double precision point which coordinates are implicitly converted
to doubles from coordinates of the specified integer point.
Get string representation of the class.
Returns string, which contains values of the point in readable form.
Calculate Euclidean norm of the vector comprised of the point's
coordinates - distance from (0, 0) in other words.
Returns point's distance from (0, 0) point.
Compares the current instance with another object of the same type and returns an integer that indicates whether the current instance precedes, follows, or occurs in the same position in the sort order as the other object.
An object to compare with this instance.
A value that indicates the relative order of the objects being compared. The return value has these meanings: Value Meaning Less than zero This instance precedes in the sort order. Zero This instance occurs in the same position in the sort order as . Greater than zero This instance follows in the sort order.
Represents an integer range with minimum and maximum values.
The class represents an integer range with inclusive limits, where
both minimum and maximum values of the range are included into it.
Mathematical notation of such range is [min, max].
// create [1, 10] range
var range1 = new IntRange(1, 10);
// create [5, 15] range
var range2 = new IntRange(5, 15);
check if values is inside of the first range
if (range1.IsInside(7))
{
// ...
}
// check if the second range is inside of the first range
if (range1.IsInside(range2))
{
// ...
}
// check if two ranges overlap
if (range1.IsOverlapping(range2))
{
// ...
}
Minimum value of the range.
Represents minimum value (left side limit) of the range [min, max].
Maximum value of the range.
Represents maximum value (right side limit) of the range [min, max].
Gets the length of the range, defined as (max - min).
Initializes a new instance of the class.
Minimum value of the range.
Maximum value of the range.
Check if the specified value is inside of the range.
Value to check.
True if the specified value is inside of the range or false otherwise.
Computes the intersection between two ranges.
The second range for which the intersection should be calculated.
An new structure containing the intersection
between this range and the given as argument.
Check if the specified range is inside of the range.
Range to check.
True if the specified range is inside of the range or false otherwise.
Check if the specified range overlaps with the range.
Range to check for overlapping.
True if the specified range overlaps with the range or false otherwise.
Determines whether two instances are equal.
Determines whether two instances are not equal.
Indicates whether the current object is equal to another object of the same type.
An object to compare with this object.
true if the current object is equal to the parameter; otherwise, false.
Determines whether the specified , is equal to this instance.
The to compare with this instance.
true if the specified is equal to this instance; otherwise, false.
Returns a hash code for this instance.
A hash code for this instance, suitable for use in hashing algorithms and data structures like a hash table.
Returns a that represents this instance.
A that represents this instance.
Returns a that represents this instance.
The format.
The format provider.
A that represents this instance.
Performs an implicit conversion from to .
The range.
The result of the conversion.
Performs an implicit conversion from to .
The range.
The result of the conversion.
Returns an enumerator that iterates through a collection.
An object that can be used to iterate through the collection.
Returns an enumerator that iterates through a collection.
An object that can be used to iterate through the collection.
Structure for representing a pair of coordinates of float type.
The structure is used to store a pair of floating point
coordinates with single precision.
Sample usage:
// assigning coordinates in the constructor
Point p1 = new Point( 10, 20 );
// creating a point and assigning coordinates later
Point p2;
p2.X = 30;
p2.Y = 40;
// calculating distance between two points
float distance = p1.DistanceTo( p2 );
X coordinate.
Y coordinate.
Initializes a new instance of the structure.
X axis coordinate.
Y axis coordinate.
Calculate Euclidean distance between two points.
Point to calculate distance to.
Returns Euclidean distance between this point and
points.
Calculate squared Euclidean distance between two points.
Point to calculate distance to.
Returns squared Euclidean distance between this point and
points.
Addition operator - adds values of two points.
First point for addition.
Second point for addition.
Returns new point which coordinates equal to sum of corresponding
coordinates of specified points.
Addition operator - adds values of two points.
First point for addition.
Second point for addition.
Returns new point which coordinates equal to sum of corresponding
coordinates of specified points.
Subtraction operator - subtracts values of two points.
Point to subtract from.
Point to subtract.
Returns new point which coordinates equal to difference of corresponding
coordinates of specified points.
Subtraction operator - subtracts values of two points.
Point to subtract from.
Point to subtract.
Returns new point which coordinates equal to difference of corresponding
coordinates of specified points.
Addition operator - adds scalar to the specified point.
Point to increase coordinates of.
Value to add to coordinates of the specified point.
Returns new point which coordinates equal to coordinates of
the specified point increased by specified value.
Addition operator - adds scalar to the specified point.
Point to increase coordinates of.
Value to add to coordinates of the specified point.
Returns new point which coordinates equal to coordinates of
the specified point increased by specified value.
Subtraction operator - subtracts scalar from the specified point.
Point to decrease coordinates of.
Value to subtract from coordinates of the specified point.
Returns new point which coordinates equal to coordinates of
the specified point decreased by specified value.
Subtraction operator - subtracts scalar from the specified point.
Point to decrease coordinates of.
Value to subtract from coordinates of the specified point.
Returns new point which coordinates equal to coordinates of
the specified point decreased by specified value.
Multiplication operator - multiplies coordinates of the specified point by scalar value.
Point to multiply coordinates of.
Multiplication factor.
Returns new point which coordinates equal to coordinates of
the specified point multiplied by specified value.
Multiplication operator - multiplies coordinates of the specified point by scalar value.
Point to multiply coordinates of.
Multiplication factor.
Returns new point which coordinates equal to coordinates of
the specified point multiplied by specified value.
Division operator - divides coordinates of the specified point by scalar value.
Point to divide coordinates of.
Division factor.
Returns new point which coordinates equal to coordinates of
the specified point divided by specified value.
Division operator - divides coordinates of the specified point by scalar value.
Point to divide coordinates of.
Division factor.
Returns new point which coordinates equal to coordinates of
the specified point divided by specified value.
Equality operator - checks if two points have equal coordinates.
First point to check.
Second point to check.
Returns if coordinates of specified
points are equal.
Inequality operator - checks if two points have different coordinates.
First point to check.
Second point to check.
Returns if coordinates of specified
points are not equal.
Check if this instance of equal to the specified one.
Another point to check equalty to.
Return if objects are equal.
Get hash code for this instance.
Returns the hash code for this instance.
Explicit conversion to .
Single precision point to convert to integer point.
Returns new integer point which coordinates are explicitly converted
to integers from coordinates of the specified single precision point by
casting float values to integers value.
Implicit conversion to .
Single precision point to convert to double precision point.
Returns new double precision point which coordinates are implicitly converted
to doubles from coordinates of the specified single precision point.
Rounds the single precision point.
Returns new integer point, which coordinates equal to whole numbers
nearest to the corresponding coordinates of the single precision point.
Get string representation of the class.
Returns string, which contains values of the point in readable form.
Calculate Euclidean norm of the vector comprised of the point's
coordinates - distance from (0, 0) in other words.
Returns point's distance from (0, 0) point.
Evaluator of expressions written in reverse polish notation.
The class evaluates expressions writen in reverse postfix polish notation.
The list of supported functuins is:
- Arithmetic functions: +, -, *, /;
- sin - sine;
- cos - cosine;
- ln - natural logarithm;
- exp - exponent;
- sqrt - square root.
Arguments for these functions could be as usual constants, written as numbers, as variables,
writen as $<var_number> ($2, for example). The variable number is zero based index
of variables array.
Sample usage:
// expression written in polish notation
string expression = "2 $0 / 3 $1 * +";
// variables for the expression
double[] vars = new double[] { 3, 4 };
// expression evaluation
double result = PolishExpression.Evaluate( expression, vars );
Evaluates specified expression.
Expression written in postfix polish notation.
Variables for the expression.
Evaluated value of the expression.
Unsupported function is used in the expression.
Incorrect postfix polish expression.
Represents a double range with minimum and maximum values,
where values are single-precision floating-point (float) numbers.
This class represents a double range with inclusive limits, where
both minimum and maximum values of the range are included into it.
Mathematical notation of such range is [min, max].
// create [0.25, 1.5] range
var range1 = new SingleRange(0.25f, 1.5f);
// create [1.00, 2.25] range
var range2 = new SingleRange(1.00f, 2.25f);
// check if values is inside of the first range
if (range1.IsInside(0.75f))
{
// ...
}
// check if the second range is inside of the first range
if (range1.IsInside(range2))
{
// ...
}
// check if two ranges overlap
if (range1.IsOverlapping(range2))
{
// ...
}
Minimum value of the range.
Represents minimum value (left side limit) of the range [min, max].
Maximum value of the range.
Represents maximum value (right side limit) of the range [min, max].
Gets the length of the range, defined as (max - min).
Initializes a new instance of the class.
Minimum value of the range.
Maximum value of the range.
Check if the specified value is inside of the range.
Value to check.
True if the specified value is inside of the range or false otherwise.
Check if the specified range is inside of the range.
Range to check.
True if the specified range is inside of the range or false otherwise.
Check if the specified range overlaps with the range.
Range to check for overlapping.
True if the specified range overlaps with the range or false otherwise.
Computes the intersection between two ranges.
The second range for which the intersection should be calculated.
An new structure containing the intersection
between this range and the given as argument.
Determines whether two instances are equal.
Determines whether two instances are not equal.
Indicates whether the current object is equal to another object of the same type.
An object to compare with this object.
true if the current object is equal to the parameter; otherwise, false.
Determines whether the specified , is equal to this instance.
The to compare with this instance.
true if the specified is equal to this instance; otherwise, false.
Returns a hash code for this instance.
A hash code for this instance, suitable for use in hashing algorithms and data structures like a hash table.
Returns a that represents this instance.
A that represents this instance.
Returns a that represents this instance.
The format.
The format provider.
A that represents this instance.
Performs an implicit conversion from to .
The range.
The result of the conversion.
Converts this single-precision range into an .
Specifies if inner integer range must be returned or outer range.
Returns integer version of the range.
If is set to , then the
returned integer range will always fit inside of the current single precision range.
If it is set to , then current single precision range will always
fit into the returned integer range.
Set of systems tools.
The class is a container of different system tools, which are used
across the framework. Some of these tools are platform specific, so their
implementation is different on different platform, like .NET and Mono.
Determines whether the code is running under Mono.
Copy block of unmanaged memory.
Destination pointer.
Source pointer.
Memory block's length to copy.
Return's value of - pointer to destination.
This function is required because of the fact that .NET does
not provide any way to copy unmanaged blocks, but provides only methods to
copy from unmanaged memory to managed memory and vise versa.
Copy block of unmanaged memory.
Destination pointer.
Source pointer.
Memory block's length to copy.
Return's value of - pointer to destination.
This function is required because of the fact that .NET does
not provide any way to copy unmanaged blocks, but provides only methods to
copy from unmanaged memory to managed memory and vise versa.
Fill memory region with specified value.
Destination pointer.
Filler byte's value.
Memory block's length to fill.
Return's value of - pointer to destination.
Fill memory region with specified value.
Destination pointer.
Filler byte's value.
Memory block's length to fill.
Return's value of - pointer to destination.
Thread safe version of the class.
The class inherits the and overrides
its random numbers generation methods providing thread safety by guarding call
to the base class with a lock. See documentation to for
additional information about the base class.
Initializes a new instance of the class.
See for more information.
Initializes a new instance of the class.
A number used to calculate a starting value for the pseudo-random number sequence.
If a negative number is specified, the absolute value of the number is used.
See for more information.
Returns a nonnegative random number.
Returns a 32-bit signed integer greater than or equal to zero and less than
.
See for more information.
Returns a nonnegative random number less than the specified maximum.
The exclusive upper bound of the random number to be generated.
must be greater than or equal to zero.
Returns a 32-bit signed integer greater than or equal to zero, and less than ;
that is, the range of return values ordinarily includes zero but not .
See for more information.
Returns a random number within a specified range.
The inclusive lower bound of the random number returned.
The exclusive upper bound of the random number returned.
must be greater than or equal to .
Returns a 32-bit signed integer greater than or equal to and less
than ; that is, the range of return values includes
but not .
See for more information.
Fills the elements of a specified array of bytes with random numbers.
An array of bytes to contain random numbers.
See for more information.
Returns a random number between 0.0 and 1.0.
Returns a double-precision floating point number greater than or equal to 0.0, and less than 1.0.
See for more information.
Specifies that an argument, in a method or function,
must be greater than zero.
Initializes a new instance of the class.
Specifies that an argument, in a method or function,
must be lesser than zero.
Initializes a new instance of the class.
Specifies that an argument, in a method or function,
must be lesser than or equal to zero.
Initializes a new instance of the class.
Specifies that an argument, in a method or function,
must be greater than or equal to zero.
Initializes a new instance of the class.
Specifies that an argument, in a method or function,
must be real (double).
Initializes a new instance of the class.
Gets the minimum allowed field value.
Gets the maximum allowed field value.
Specifies that an argument, in a method or function,
must be real between 0 and 1.
Initializes a new instance of the class.
Specifies that an argument, in a method or function,
must be an integer bigger than zero.
Initializes a new instance of the class.
Specifies that an argument, in a method or function,
must be an integer less than zero.
Initializes a new instance of the class.
Specifies that an argument, in a method or function,
must be an integer smaller than or equal to zero.
Initializes a new instance of the class.
Specifies that an argument, in a method or function,
must be an integer bigger than or equal to zero.
Initializes a new instance of the class.
Specifies that an argument, in a method or function,
must be an integer.
Initializes a new instance of the class.
Gets the minimum allowed field value.
Gets the maximum allowed field value.
Runtime cast.
The target type.
The source type.
Gets the value being casted.
Initializes a new instance of the struct.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Runtime cast.
The target type.
Gets the value being casted.
Initializes a new instance of the struct.
The value.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Performs an implicit conversion from to .
The value.
The result of the conversion.
Static class for utility extension methods.
Creates and adds multiple
objects with the given names at once.
The
to add in.
The names of the to
be created and added.
DataTable table = new DataTable();
// Add multiple columns at once:
table.Columns.Add("columnName1", "columnName2");
Creates and adds multiple
objects with the given names at once.
The
to add in.
The names of the s to
be created and added, alongside with their types.
DataTable table = new DataTable();
// Add multiple columns at once:
table.Columns.Add(new OrderedDictionary>String, Type<()
{
{ "columnName1", typeof(int) },
{ "columnName2", typeof(double) },
});
Gets a the value of a
associated with a particular enumeration value.
The enumeration type.
The enumeration value.
The string value stored in the value's description attribute.
Reads a struct from a stream.
Reads a struct from a stream.
Reads a struct from a stream.
Reads a struct from a stream.
Reads a struct from a stream.
Reads a struct from a stream.
Reads a struct from a stream.
Gets the underlying buffer position for a StreamReader.
A StreamReader whose position will be retrieved.
The current offset from the beginning of the
file that the StreamReader is currently located into.
Deserializes the specified stream into an object graph, but locates
types by searching all loaded assemblies and ignoring their versions.
The binary formatter.
The stream from which to deserialize the object graph.
The top (root) of the object graph.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
The destination type.
The value to be converted.
The result of the conversion.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
The value to be converted.
The type that the value should be converted to.
The result of the conversion.
Determines whether the given type has a public default (parameterless) constructor.
The type to check.
True if the type has a public parameterless constructor; false otherwise.
Replaces the format item in a specified string with the string
representation of a corresponding object in a specified array.
A composite format string.
An object array that contains zero or more objects to format.
A copy of str in which the format items have been replaced by
the string representation of the corresponding objects in args.
Checks whether two dictionaries have the same contents.
Checks whether an object implements a method with the given name.
Checks whether a type implements a method with the given name.
Determines whether a > b.
Determines whether a >= b.
Determines whether a < b.
Determines whether a <= b.
Gets the default value for a type. This method should serve as
a programmatic equivalent to default(T).
The type whose default value should be retrieved.
Retrieves the memory address of a generic value type.
The type of the object whose address needs to be retrieved.
The object those address needs to be retrieved.
Retrieves the memory address of a generic reference type.
The type of the object whose address needs to be retrieved.
The object those address needs to be retrieved.
Attempts to download a file from the web multiple times before giving up.
The web client to use.
The URL of the file to be downloaded.
The disk location where the file should be stored.
The maximum number of attempts.
Do not overwrite if it already exists.
Algorithm Convergence Exception.
The algorithm convergence exception is thrown in cases where a iterative
algorithm could not converge to a finite solution.
Initializes a new instance of the class.
Initializes a new instance of the class.
Message providing some additional information.
Initializes a new instance of the class.
Message providing some additional information.
The exception that is the cause of the current exception.
Initializes a new instance of the class.
The that holds the serialized object data about the exception being thrown.
The that contains contextual information about the source or destination.
The parameter is null.
The class name is null or is zero (0).
Dimension Mismatch Exception.
The dimension mismatch exception is thrown in cases where a method expects
a matrix or array object having specific or compatible dimensions, such as the inner matrix
dimensions in matrix multiplication.
Initializes a new instance of the class.
Initializes a new instance of the class.
The name of the parameter that caused the current exception.
Initializes a new instance of the class.
The name of the parameter that caused the current exception.
Message providing some additional information.
Initializes a new instance of the class.
Message providing some additional information.
The exception that is the cause of the current exception.
Initializes a new instance of the class.
The that holds the serialized object data about the exception being thrown.
The that contains contextual information about the source or destination.
The parameter is null.
The class name is null or is zero (0).
Non-Positive Definite Matrix Exception.
The non-positive definite matrix exception is thrown in cases where a method
expects a matrix to have only positive eigenvalues, such when dealing with covariance matrices.
Initializes a new instance of the class.
Initializes a new instance of the class.
Message providing some additional information.
Initializes a new instance of the class.
Message providing some additional information.
The exception that is the cause of the current exception.
Initializes a new instance of the class.
The that holds the serialized object data about the exception being thrown.
The that contains contextual information about the source or destination.
The parameter is null.
The class name is null or is zero (0).
Non-Symmetric Matrix Exception.
The not symmetric matrix exception is thrown in cases where a method
expects a matrix to be symmetric but it is not.
Initializes a new instance of the class.
Initializes a new instance of the class.
Message providing some additional information.
Initializes a new instance of the class.
Message providing some additional information.
The exception that is the cause of the current exception.
Initializes a new instance of the class.
The that holds the serialized object data about the exception being thrown.
The that contains contextual information about the source or destination.
The parameter is null.
The class name is null or is zero (0).
Singular Matrix Exception.
The singular matrix exception is thrown in cases where a method which
performs matrix inversions has encountered a non-invertible matrix during the process.
Initializes a new instance of the class.
Initializes a new instance of the class.
Message providing some additional information.
Initializes a new instance of the class.
Message providing some additional information.
The exception that is the cause of the current exception.
Initializes a new instance of the class.
The that holds the serialized object data about the exception being thrown.
The that contains contextual information about the source or destination.
The parameter is null.
The class name is null or is zero (0).
Common interface for Range objects, such as ,
or . A range represents the
interval between two values in the form [min, max].
The type of the range.
Minimum value of the range.
Represents minimum value (left side limit) of the range [min, max].
Maximum value of the range.
Represents maximum value (right side limit) of the range [min, max].
Model serializer. Can be used to serialize and deserialize (i.e. save and
load) models from the framework to and from the disk and other streams.
This class uses a binding mechanism to automatically convert files saved using
older versions of the framework to the new format. If a deserialization doesn't
work, please fill in a bug report at https://github.com/accord-net/framework/issues
The first example shows the simplest way to use the serializer to persist objects: