TransformFunctions¶

Concatenate
Rearrange
Reduce
LinearCombination
CombineMeans
MatrixTransform
PredictionErrorDeltaFunction

Overview¶

Functions that combine multiple items with the same shape, yielding a result with a single item that has the same shape as the individual items.

All Transformfunctions must have two attributes - multiplicative_param and additive_param - each of which is assigned the name of one of the function’s parameters; this is for use by ModulatoryProjections (and, in particular, GatingProjections, when the TransformFunction is used as the function of an InputPort or OutputPort).

class psyneulink.core.components.functions.transformfunctions.Concatenate(default_variable=None, scale=None, offset=None, params=None, owner=None, prefs=None)¶

Concatenates items in outer dimension (axis 0) of variable into a single array, optionally scaling and/or adding an offset to the result after concatenating.

function returns a 1d array with length equal to the sum of the lengths of the items in variable.

derivative returns scale.

Parameters:

default_variable (list or np.array : default class_defaults.variable) – specifies a template for the value to be transformed and its default value; all entries must be numeric.
scale (float) – specifies a value by which to multiply each element of the output of function (see scale for details)
offset (float) – specifies a value to add to each element of the output of function (see offset for details)
params (Dict[param keyword: param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
owner (Component) – component to which to assign the Function.
name (str : default see name) – specifies the name of the Function.
prefs (PreferenceSet or specification dict : default Function.classPreferences) – specifies the PreferenceSet for the Function (see prefs for details).

default_variable¶

contains template of array(s) to be concatenated.

Type:: list or np.array

scale¶

value is applied multiplicatively to each element of the concatenated, before applying the offset (if it is specified).

Type:: float

offset¶

value is added to each element of the concatentated array, after scale has been applied (if it is specified).

Type:: float

owner¶

component to which the Function has been assigned.

Type:: Component

name¶

the name of the Function; if it is not specified in the name argument of the constructor, a default is assigned by FunctionRegistry (see Naming for conventions used for default and duplicate names).

Type:: str

prefs¶

the PreferenceSet for function; if it is not specified in the prefs argument of the Function’s constructor, a default is assigned using classPreferences defined in __init__.py (see Preferences for details).

Type:: PreferenceSet or specification dict : Function.classPreferences

_function(variable=None, context=None, params=None)¶

Use numpy hstack to concatenate items in outer dimension (axis 0) of variable.

Parameters:

variable (list or np.array : default class_defaults.variable) – a list or np.array of numeric values.
params (Dict[param keyword: param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.

Returns:

Concatenated array of items in variable (array) – in an array that is one dimension less than variable.

_validate_params(request_set, target_set=None, context=None)¶

Validate scale and offset parameters

Check that SCALE and OFFSET are scalars.

_validate_variable(variable, context=None)¶

Insure that list or array is 1d and that all elements are numeric

Parameters:

variable
context

derivative(input)¶

Derivative of function at input.

Parameters:

input (number) – value of the input to the function at which derivative is to be taken.
covariates (2d np.array : default class_defaults.variable[1:]) – the input(s) to the Concatenate function other than the one for which the derivative is being computed; these are ignored and are accepted for consistency with other functions.

Returns:

Scale of function (number or array)

class psyneulink.core.components.functions.transformfunctions.CombineMeans(default_variable=None, weights=None, exponents=None, operation=None, scale=None, offset=None, params=None, owner=None, prefs=None)¶

Calculate and combine mean(s) for arrays of values, optionally weighting and/or exponentiating each mean prior to combining, and scaling and/or offsetting after combining.

function takes the mean of each array in the outermost dimension (axis 0) of variable, and combines them either additively or multiplicatively (as specified by operation), applying weights and/or exponents (if specified) to each mean prior to combining them, and applying scale and/or offeset (if specified) to the result after combining, and returns a scalar value.

Parameters:

variable (1d or 2d np.array : default class_defaults.variable) – specifies a template for the arrays to be combined. If it is 2d, all items must have the same length.
weights (1d or 2d np.array : default None) – specifies values used to multiply the elements of each array in variable. If it is 1d, its length must equal the number of items in variable; if it is 2d, the length of each item must be the same as those in variable, and there must be the same number of items as there are in variable (see weights for details)
exponents (1d or 2d np.array : default None) – specifies values used to exponentiate the elements of each array in variable. If it is 1d, its length must equal the number of items in variable; if it is 2d, the length of each item must be the same as those in variable, and there must be the same number of items as there are in variable (see exponents for details)
operation (SUM or PRODUCT : default SUM) – specifies whether the function takes the sum or product of the means of the arrays in variable.
scale (float or np.ndarray : default None) – specifies a value by which to multiply the result of function (see scale for details)
offset (float or np.ndarray : default None) – specifies a value to add to the result of function (see offset for details)
params (Dict[param keyword: param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
owner (Component) – component to which to assign the Function.
name (str : default see name) – specifies the name of the Function.
prefs (PreferenceSet or specification dict : default Function.classPreferences) – specifies the PreferenceSet for the Function (see prefs for details).

variable¶

contains the arrays to be combined by function. If it is 1d, the array is simply linearly transformed by and scale and offset. If it is 2d, the arrays (all of which must be of equal length) are weighted and/or exponentiated as specified by weights and/or exponents and then combined as specified by operation.

Type:: 1d or 2d np.array

weights¶

if it is 1d, each element is used to multiply all elements in the corresponding array of variable; if it is 2d, then each array is multiplied elementwise (i.e., the Hadamard Product is taken) with the corresponding array of variable. All weights are applied before any exponentiation (if it is specified).

Type:: 1d or 2d np.array : default NOne

exponents¶

if it is 1d, each element is used to exponentiate the elements of the corresponding array of variable; if it is 2d, the element of each array is used to exponentiate the corresponding element of the corresponding array of variable. In either case, exponentiating is applied after application of the weights (if any are specified).

Type:: 1d or 2d np.array : default None

operation¶

determines whether the function takes the elementwise (Hadamard) sum or product of the arrays in variable.

Type:: SUM or PRODUCT : default SUM

scale¶

value is applied multiplicatively to each element of the array after applying the operation (see scale for details); this done before applying the offset (if it is specified).

Type:: float or np.ndarray : default None

offset¶

value is added to each element of the array after applying the operation and scale (if it is specified).

Type:: float or np.ndarray : default None

owner¶

component to which the Function has been assigned.

Type:: Component

name¶

the name of the Function; if it is not specified in the name argument of the constructor, a default is assigned by FunctionRegistry (see Naming for conventions used for default and duplicate names).

Type:: str

prefs¶

the PreferenceSet for function; if it is not specified in the prefs argument of the Function’s constructor, a default is assigned using classPreferences defined in __init__.py (see Preferences for details).

Type:: PreferenceSet or specification dict : Function.classPreferences

_function(variable=None, context=None, params=None)¶

Parameters:

variable (1d or 2d np.array : default class_defaults.variable) – a single numeric array, or multiple arrays to be combined; if it is 2d, all arrays must have the same length.
params (Dict[param keyword: param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.

Returns:

combined means (number) – the result of taking the means of each array in variable and combining them.

_validate_params(request_set, target_set=None, context=None)¶

Validate weights, exponents, scale and offset parameters

Check that WEIGHTS and EXPONENTS are lists or np.arrays of numbers with length equal to variable Check that SCALE and OFFSET are either scalars or np.arrays of numbers with length and shape equal to variable

Note: the checks of compatibility with variable are only performed for validation calls during execution: (i.e., from check_args(), since during initialization or COMMAND_LINE assignment, a parameter may be re-assigned before variable assigned during is known

_validate_variable(variable, context=None)¶: Insure that all items of variable are numeric

class psyneulink.core.components.functions.transformfunctions.Rearrange(default_variable=None, scale=None, offset=None, arrangement=None, params=None, owner=None, prefs=None)¶

Rearranges items in outer dimension (axis 0) of variable, as specified by arrangement, optionally scaling and/or adding an offset to the result after concatenating.

The arrangement argument specifies how to rearrange the items of variable, possibly concatenating subsets of them into single 1d arrays. The specification must be an integer, a tuple of integers, or a list containing either or both. Each integer must be an index of an item in the outer dimension (axis 0) of variable. Items referenced in a tuple are concatenated in the order specified into a single 1d array, and that 1d array is included in the resulting 2d array in the order it appears in arrangement. If arrangement is specified, then only the items of variable referenced in the specification are included in the result; if arrangement is not specified, all of the items of variable are concatenated into a single 1d array (i.e., it functions identically to Concatenate).

function returns a 2d array with the items of variable rearranged (and possibly concatenated) as specified by arrangement.

Examples

>>> r = Rearrange(arrangement=[(1,2),(0)])
>>> print(r(np.array([[0,0],[1,1],[2,2]])))
[array([1., 1., 2., 2.]) array([0., 0.])]

>>> r = Rearrange()
>>> print(r(np.array([[0,0],[1,1],[2,2]])))
[0. 0. 1. 1. 2. 2.]

Parameters:

default_variable (list or np.array : default class_defaults.variable) – specifies a template for the value to be transformed and its default value; all entries must be numeric.
arrangement (int, tuple, or list : default None) – specifies ordering of items in variable and/or ones to concatenate. (see above for details).
scale (float) – specifies a value by which to multiply each element of the output of function (see scale for details).
offset (float) – specifies a value to add to each element of the output of function (see offset for details).
params (Dict[param keyword: param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
owner (Component) – component to which to assign the Function.
name (str : default see name) – specifies the name of the Function.
prefs (PreferenceSet or specification dict : default Function.classPreferences) – specifies the PreferenceSet for the Function (see prefs for details).

default_variable¶

contains template of array(s) to be concatenated.

Type:: list or np.array

arrangement¶

determines ordering of items in variable and/or ones to concatenate (see above for additional details).

Type:: list of one or more tuples

scale¶

value is applied multiplicatively to each element of the concatenated, before applying the offset (if it is specified).

Type:: float

offset¶

value is added to each element of the concatentated array, after scale has been applied (if it is specified).

Type:: float

owner¶

component to which the Function has been assigned.

Type:: Component

name¶

the name of the Function; if it is not specified in the name argument of the constructor, a default is assigned by FunctionRegistry (see Naming for conventions used for default and duplicate names).

Type:: str

prefs¶

the PreferenceSet for function; if it is not specified in the prefs argument of the Function’s constructor, a default is assigned using classPreferences defined in __init__.py (see Preferences for details).

Type:: PreferenceSet or specification dict : Function.classPreferences

_function(variable=None, context=None, params=None)¶

Rearrange items in outer dimension (axis 0) of variable according to arrangement.

Parameters:

variable (list or np.array : default class_defaults.variable) – a list or np.array of numeric values.
params (Dict[param keyword: param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.

Returns:

Rearranged items of outer dimension (axis 0) of **variable** (array) – in a 2d array.

_instantiate_attributes_before_function(function=None, context=None)¶

Insure all items of arrangement are tuples and compatibility with default_variable

If arrangement is specified, convert all items to tuples If default_variable is NOT specified, assign with length in outer dimension = max index in arragnement If default_variable IS _user_specified, compatiblility with arrangement is checked in _validate_params

_validate_params(request_set, target_set=None, context=None)¶: Validate arrangement, scale and offset parameters

_validate_variable(variable, context=None)¶: Insure that all elements are numeric and that list or array is at least 2d

class psyneulink.core.components.functions.transformfunctions.Reduce(weights=None, exponents=None, default_variable=None, operation=None, scale=None, offset=None, params=None, owner=None, prefs=None)¶

Combines values in each of one or more arrays into a single value for each array, with optional weighting and/or exponentiation of each item within an array prior to combining, and scaling and/or offset of result after combining.

function returns an array of scalar values, one for each array in variable.

Parameters:

default_variable (list or np.array : default class_defaults.variable) – specifies a template for the value to be transformed and its default value; all entries must be numeric.
weights (1d or 2d np.array : default None) – specifies values used to multiply the elements of each array in variable. If it is 1d, its length must equal the number of items in variable; if it is 2d, the length of each item must be the same as those in variable, and there must be the same number of items as there are in variable (see weights for details)
exponents (1d or 2d np.array : default None) – specifies values used to exponentiate the elements of each array in variable. If it is 1d, its length must equal the number of items in variable; if it is 2d, the length of each item must be the same as those in variable, and there must be the same number of items as there are in variable (see exponents for details)
operation (SUM or PRODUCT : default SUM) – specifies whether to sum or multiply the elements in variable of function.
scale (float) – specifies a value by which to multiply each element of the output of function (see scale for details)
offset (float) – specifies a value to add to each element of the output of function (see offset for details)
params (Dict[param keyword: param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
owner (Component) – component to which to assign the Function.
name (str : default see name) – specifies the name of the Function.
prefs (PreferenceSet or specification dict : default Function.classPreferences) – specifies the PreferenceSet for the Function (see prefs for details).

default_variable¶

contains array(s) to be reduced.

Type:: list or np.array

operation¶

determines whether elements of each array in variable of function are summmed or multiplied.

Type:: SUM or PRODUCT

scale¶

value is applied multiplicatively to each element of the array after applying the operation (see scale for details); this done before applying the offset (if it is specified).

Type:: float

offset¶

value is added to each element of the array after applying the operation and scale (if it is specified).

Type:: float

owner¶

component to which the Function has been assigned.

Type:: Component

name¶

the name of the Function; if it is not specified in the name argument of the constructor, a default is assigned by FunctionRegistry (see Naming for conventions used for default and duplicate names).

Type:: str

prefs¶

the PreferenceSet for function; if it is not specified in the prefs argument of the Function’s constructor, a default is assigned using classPreferences defined in __init__.py (see Preferences for details).

Type:: PreferenceSet or specification dict : Function.classPreferences

_function(variable=None, context=None, params=None)¶

Parameters:

variable (list or np.array : default class_defaults.variable) – a list or np.array of numeric values.
params (Dict[param keyword: param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.

Returns:

Sum or product of arrays in variable (array) – in an array that is one dimension less than variable.

_validate_params(request_set, target_set=None, context=None)¶

Validate weghts, exponents, scale and offset parameters

Check that WEIGHTS and EXPONENTS are lists or np.arrays of numbers with length equal to variable. Check that SCALE and OFFSET are scalars.

Note: the checks of compatibility with variable are only performed for validation calls during execution: (i.e., from check_args(), since during initialization or COMMAND_LINE assignment, a parameter may be re-assigned before variable assigned during is known

_validate_variable(variable, context=None)¶

Insure that list or array is 1d and that all elements are numeric

Parameters:

variable
context

class psyneulink.core.components.functions.transformfunctions.LinearCombination(default_variable=None, weights=None, exponents=None, operation=None, scale=None, offset=None, params=None, owner=None, prefs=None)¶

Linearly combine arrays of values, optionally weighting and/or exponentiating each array prior to combining, and scaling and/or offsetting after combining.

function combines the arrays in the outermost dimension (axis 0) of variable either additively or multiplicatively (as specified by operation), applying weights and/or exponents (if specified) to each array prior to combining them, and applying scale and/or offeset (if specified) to the result after combining, and returns an array of the same length as the operand arrays.

Parameters:

variable (1d or 2d np.array : default class_defaults.variable) – specifies a template for the arrays to be combined. If it is 2d, all items must have the same length.
weights (scalar or 1d or 2d np.array : default None) – specifies values used to multiply the elements of each array in variable. If it is 1d, its length must equal the number of items in variable; if it is 2d, the length of each item must be the same as those in variable, and there must be the same number of items as there are in variable (see weights for details of how weights are applied).
exponents (scalar or 1d or 2d np.array : default None) – specifies values used to exponentiate the elements of each array in variable. If it is 1d, its length must equal the number of items in variable; if it is 2d, the length of each item must be the same as those in variable, and there must be the same number of items as there are in variable (see exponents for details of how exponents are applied).
operation (SUM, PRODUCT or CROSS_ENTROPY : default SUM) – specifies whether the function takes the elementwise (Hadamarad) sum, product or cross entropy of the arrays in variable.
scale (float or np.ndarray : default None) – specifies a value by which to multiply each element of the result of function (see scale for details)
offset (float or np.ndarray : default None) – specifies a value to add to each element of the result of function (see offset for details)
params (Dict[param keyword: param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
owner (Component) – component to which to assign the Function.
name (str : default see name) – specifies the name of the Function.
prefs (PreferenceSet or specification dict : default Function.classPreferences) – specifies the PreferenceSet for the Function (see prefs for details).

variable¶

contains the arrays to be combined by function. If it is 1d, the array is simply linearly transformed by and scale and offset. If it is 2d, the arrays (all of which must be of equal length) are weighted and/or exponentiated as specified by weights and/or exponents and then combined as specified by operation.

Type:: 1d or 2d np.array

weights¶

if it is a scalar, the value is used to multiply all elements of all arrays in variable; if it is a 1d array, each element is used to multiply all elements in the corresponding array of variable; if it is a 2d array, then each array is multiplied elementwise (i.e., the Hadamard Product is taken) with the corresponding array of variable. All weights are applied before any exponentiation (if it is specified).

Type:: scalar or 1d or 2d np.array

exponents¶

if it is a scalar, the value is used to exponentiate all elements of all arrays in variable; if it is a 1d array, each element is used to exponentiate the elements of the corresponding array of variable; if it is a 2d array, the element of each array is used to exponentiate the corresponding element of the corresponding array of variable. In either case, all exponents are applied after application of the weights (if any are specified).

Type:: scalar or 1d or 2d np.array

operation¶

determines whether the function takes the elementwise (Hadamard) sum, product, or cross entropy of the arrays in variable.

Type:: SUM or PRODUCT

scale¶

value is applied multiplicatively to each element of the array after applying the operation (see scale for details); this done before applying the offset (if it is specified).

Type:: float or np.ndarray

offset¶

value is added to each element of the array after applying the operation and scale (if it is specified).

Type:: float or np.ndarray

owner¶

component to which the Function has been assigned.

Type:: Component

name¶

the name of the Function; if it is not specified in the name argument of the constructor, a default is assigned by FunctionRegistry (see Naming for conventions used for default and duplicate names).

Type:: str

prefs¶

the PreferenceSet for function; if it is not specified in the prefs argument of the Function’s constructor, a default is assigned using classPreferences defined in __init__.py (see Preferences for details).

Type:: PreferenceSet or specification dict : Function.classPreferences

_function(variable=None, context=None, params=None)¶

Parameters:

variable (1d or 2d np.array : default class_defaults.variable) – a single numeric array, or multiple arrays to be combined; if it is 2d, all arrays must have the same length.
params (Dict[param keyword: param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.

Returns:

combined array (1d array) – the result of linearly combining the arrays in variable.

_validate_params(request_set, target_set=None, context=None)¶

Validate weghts, exponents, scale and offset parameters

Check that WEIGHTS and EXPONENTS are lists or np.arrays of numbers with length equal to variable Check that SCALE and OFFSET are either scalars or np.arrays of numbers with length and shape equal to variable

Note: the checks of compatibility with variable are only performed for validation calls during execution: (i.e., from check_args(), since during initialization or COMMAND_LINE assignment, a parameter may be re-assigned before variable assigned during is known

_validate_variable(variable, context=None)¶

Insure that all items of list or np.array in variable are of the same length

Parameters:

variable
context

derivative(input)¶

Derivative of function at input.

Parameters:

output (1d np.array : default class_defaults.variable[0]) –
value of the input to the Linear transform at which derivative is to be taken. a single numeric array or multiple arrays being combined, and at which derivative is to be taken.

output arg is used for consistency with other derivatives used by BackPropagation, and is ignored.
covariates (2d np.array : default class_defaults.variable[1:]) – the input(s) to the LinearCombination function other than the one for which the derivative is being computed; these are used to calculate the Jacobian of the LinearCombination function.

Returns:

Scale (number (if input is 1d) or array (if input is 2d))

class psyneulink.core.components.functions.transformfunctions.MatrixTransform(default_variable=None, matrix=None, operation=None, normalize=None, params=None, owner=None, prefs=None)¶

Matrix transform of variable.

function returns a matrix transform of variable: based on the operation argument.

operation = DOT_PRODUCT:

Returns the dot (inner) product of variable and matrix:

\[{variable} \bullet |matrix|\]

If normalize =True, the result is normalized by the product of the norms of the variable and matrix:

\[\frac{variable \bullet matrix}{\|variable\| \cdot \|matrix\|}\]

Note

For normalize =True, the result is the same as the cosine of the angle between pairs of vectors.

operation = L0:

Returns the absolute value of the difference between variable and matrix:

\[|variable - matrix|\]

If normalize =True, the result is normalized by the norm of the sum of differences between the variable and matrix, which is then subtracted from 1:

\[1 - \frac{|variable - matrix|}{\|variable - matrix\|}\]

Note

For normalize =True, the result has the same effect as the normalized DOT_PRODUCT operation, with more similar pairs of vectors producing larger values (closer to 1).

Warning

For normalize =False, the result is smaller (closer to 0) for more similar pairs of vectors, which is opposite the effect of the DOT_PRODUCT and normalized L0 operations. If the desired result is that more similar pairs of vectors produce larger values, set normalize =True or use the DOT_PRODUCT operation.

Parameters:

variable (list or 1d array : default class_defaults.variable) – specifies a template for the value to be transformed; length must equal the number of rows of matrix.
matrix (number, list, 1d or 2d np.ndarray, function, or matrix keyword : default IDENTITY_MATRIX) –
specifies matrix used to transform variable (see matrix for specification details).

When MatrixTransform is the function of a projection:
- the matrix specification must be compatible with the variables of the sender and receiver
- a matrix keyword specification generates a matrix based on the sender and receiver shapes
When MatrixTransform is instantiated on its own, or as the function of a Mechanism or Port:
- the matrix specification must be compatible with the function’s own variable
- if matrix is not specified, a square identity matrix is generated based on the number of columns in variable
- matrix keywords are not valid matrix specifications
operation (DOT_PRODUCT or L0 : default DOT_PRODUCT) – specifies whether to take the dot product or difference of variable and matrix.
normalize (bool : default False) – specifies whether to normalize the result of function by dividing it by the norm of variable x the norm of matrix; this cannot be used if variable is a scalar (i.e., has only one element), and operation is set to L0 (since it is not needed, and can produce a divide by zero error).
params (Dict[param keyword: param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.
owner (Component) – component to which to assign the Function.
name (str : default see name) – specifies the name of the Function.
prefs (PreferenceSet or specification dict : default Function.classPreferences) – specifies the PreferenceSet for the Function (see prefs for details).

variable¶

contains value to be transformed.

Type:: 1d array

matrix¶

matrix used to transform variable. Can be specified as any of the following:

number - used as the filler value for all elements of the matrix (call to np.fill);

list of arrays, 2d array - assigned as the value of matrix;

matrix keyword - see MatrixKeywords for list of options.

Rows correspond to elements of the input array (outer index), and columns correspond to elements of the output array (inner index).

Type:: 2d array

operation¶

determines whether dot product or difference of variable and matrix is taken. If the length of variable is greater than 1 and L0 is specified, the variable array is subtracted from each array of matrix and the resulting array is summed, to produce the corresponding element of the array returned by the function.

Type:: DOT_PRODUCT or L0 : default DOT_PRODUCT

normalize¶

determines whether the result of function is normalized, by dividing it by the norm of variable x the norm of matrix.

Type:: bool

owner¶

component to which the Function has been assigned.

Type:: Component

name¶

the name of the Function; if it is not specified in the name argument of the constructor, a default is assigned by FunctionRegistry (see Naming for conventions used for default and duplicate names).

Type:: str

prefs¶

the PreferenceSet for function; if it is not specified in the prefs argument of the Function’s constructor, a default is assigned using classPreferences defined in __init__.py (see PreferenceSet for details).

Type:: PreferenceSet or specification dict : Function.classPreferences

_function(variable=None, context=None, params=None)¶

Parameters:

variable (list or 1d array) – array to be transformed; length must equal the number of rows of matrix.
params (Dict[param keyword: param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.

Returns:

dot product of or difference between variable and matrix (1d array) – length of the array returned equals the number of columns of matrix.

_model_spec_generic_type_name = 'onnx::MatMul'¶: string describing this class’s generic type in universal model specification, if it exists and is different than the class name

_validate_params(request_set, target_set=None, context=None)¶

Validate params and assign to targets

This overrides the class method, to perform more detailed type checking (see explanation in class method). Note: this method (or the class version) is called only if the parameter_validation attribute is True

Parameters:

request_set – (dict) - params to be validated
target_set – (dict) - destination of validated params
context – (str)

Return none:

instantiate_matrix(specification, context=None)¶

Implements matrix indicated by specification

Specification is derived from MATRIX param (passed to self.__init__ or self._function)

Specification (validated in _validate_params):

single number (used to fill self.matrix)

matrix keyword (see get_matrix)

2D list or np.ndarray of numbers

Return matrix:: (2D list)

class psyneulink.core.components.functions.transformfunctions.PredictionErrorDeltaFunction(default_variable=None, gamma=None, params=None, owner=None, prefs=None)¶

Calculate temporal difference prediction error.

function returns the prediction error using arrays in variable:

\[\delta(t) = r(t) + \gamma sample(t) - sample(t - 1)\]

_function(variable=None, context=None, params=None)¶

Parameters:

variable (2d np.array : default class_defaults.variable) – a 2d array representing the sample and target values to be used to calculate the temporal difference delta values. Both arrays must have the same length
params (Dict[param keyword, param value] : default None) – a parameter dictionary that specifies the parameters for the function. Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.

Returns:

delta values

Return type:

1d np.array

_validate_params(request_set, target_set=None, context=None)¶

Checks that WEIGHTS is a list or np.array of numbers with length equal to variable.

Note: the checks of compatibility with variable are only performed for validation calls during execution (i.e. from check_args()), since during initialization or COMMAND_LINE assignment, a parameter may be re-assigned before variable assigned during is known

Parameters:

request_set
target_set
context

Return type:

None

_validate_variable(variable, context=None)¶

Insure that all items of variable are numeric

Parameters:

variable
context

Return type:

variable if all items are numeric