zfit.param module

class zfit.param.ConstantParameter(*args, **kwargs)

Bases: zfit.core.parameter.OverloadableMixin, zfit.core.parameter.ZfitParameterMixin, zfit.core.parameter.BaseParameter

Constant parameter. Value cannot change.

Parameters

• name –

• value –

• dtype –

property shape

value()

Return type

Tensor

read_value()

Return type

Tensor

property floating

property independent

Return type

bool

property static_value

class SaveSliceInfo(full_name=None, full_shape=None, var_offset=None, var_shape=None, save_slice_info_def=None, import_scope=None)

Bases: object

Information on how to save this Variable as a slice.

Provides internal support for saving variables as slices of a larger variable. This API is not public and is subject to change.

Available properties:

• full_name

• full_shape

• var_offset

• var_shape

Create a SaveSliceInfo.

Parameters

• full_name – Name of the full variable of which this Variable is a slice.

• full_shape – Shape of the full variable, as a list of int.

• var_offset – Offset of this Variable into the full variable, as a list of int.

• var_shape – Shape of this Variable, as a list of int.

• save_slice_info_def – SaveSliceInfoDef protocol buffer. If not None, recreates the SaveSliceInfo object its contents. save_slice_info_def and other arguments are mutually exclusive.

• import_scope – Optional string. Name scope to add. Only used when initializing from protocol buffer.

property spec

Computes the spec string used for saving.

to_proto(export_scope=None)

Returns a SaveSliceInfoDef() proto.

Parameters

export_scope – Optional string. Name scope to remove.

Returns

A SaveSliceInfoDef protocol buffer, or None if the Variable is not in the specified name scope.

__iter__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register __getitem__ as an overloaded operator, Python will valiantly attempt to iterate over the variable’s Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises

TypeError – when invoked.

__ne__(other)

Compares two variables element-wise for equality.

add_cache_deps(cache_deps, allow_non_cachable=True)

Add dependencies that render the cache invalid if they change.

Parameters

• cache_deps (Union[ForwardRef, Iterable[ForwardRef]]) –

• allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.

Raises

TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.

property aggregation

assign(value, use_locking=False, name=None, read_value=True)

Assigns a new value to the variable.

This is essentially a shortcut for assign(self, value).

Parameters

• value – A Tensor. The new value for this variable.

• use_locking – If True, use locking during the assignment.

• name – The name of the operation to be created

• read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.

Returns

The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

assign_add(delta, use_locking=False, name=None, read_value=True)

Adds a value to this variable.

This is essentially a shortcut for assign_add(self, delta).

Parameters

• delta – A Tensor. The value to add to this variable.

• use_locking – If True, use locking during the operation.

• name – The name of the operation to be created

• read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.

Returns

The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

assign_sub(delta, use_locking=False, name=None, read_value=True)

Subtracts a value from this variable.

This is essentially a shortcut for assign_sub(self, delta).

Parameters

• delta – A Tensor. The value to subtract from this variable.

• use_locking – If True, use locking during the operation.

• name – The name of the operation to be created

• read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.

Returns

The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

batch_scatter_update(sparse_delta, use_locking=False, name=None)

Assigns tf.IndexedSlices to this variable batch-wise.

Analogous to batch_gather. This assumes that this variable and the sparse_delta IndexedSlices have a series of leading dimensions that are the same for all of them, and the updates are performed on the last dimension of indices. In other words, the dimensions should be the following:

num_prefix_dims = sparse_delta.indices.ndims - 1 batch_dim = num_prefix_dims + 1 `sparse_delta.updates.shape = sparse_delta.indices.shape + var.shape[

batch_dim:]`

where

sparse_delta.updates.shape[:num_prefix_dims] == sparse_delta.indices.shape[:num_prefix_dims] == var.shape[:num_prefix_dims]

And the operation performed can be expressed as:

`var[i_1, …, i_n,

sparse_delta.indices[i_1, …, i_n, j]] = sparse_delta.updates[

i_1, …, i_n, j]`

When sparse_delta.indices is a 1D tensor, this operation is equivalent to scatter_update.

To avoid this operation one can looping over the first ndims of the variable and using scatter_update on the subtensors that result of slicing the first dimension. This is a valid option for ndims = 1, but less efficient than this implementation.

Parameters

• sparse_delta – tf.IndexedSlices to be assigned to this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

property constraint

Returns the constraint function associated with this variable.

Returns

The constraint function that was passed to the variable constructor. Can be None if no constraint was passed.

copy(deep=False, name=None, **overwrite_params)

Return type

ZfitObject

count_up_to(limit)

Increments this variable until it reaches limit. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Dataset.range instead.

When that Op is run it tries to increment the variable by 1. If incrementing the variable would bring it above limit then the Op raises the exception OutOfRangeError.

If no error is raised, the Op outputs the value of the variable before the increment.

This is essentially a shortcut for count_up_to(self, limit).

Parameters

limit – value at which incrementing the variable raises an error.

Returns

A Tensor that will hold the variable value before the increment. If no other Op modifies this variable, the values produced will all be distinct.

property device

The device of this variable.

property dtype

The dtype of the object

Return type

DType

eval(session=None)

In a session, computes and returns the value of this variable.

This is not a graph construction method, it does not add ops to the graph.

This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used. See tf.compat.v1.Session for more information on launching a graph and on sessions.

```python v = tf.Variable([1, 2]) init = tf.compat.v1.global_variables_initializer()

with tf.compat.v1.Session() as sess:

sess.run(init) # Usage passing the session explicitly. print(v.eval(sess)) # Usage with the default session. The ‘with’ block # above makes ‘sess’ the default session. print(v.eval())

```

Parameters

session – The session to use to evaluate this variable. If none, the default session is used.

Returns

A numpy ndarray with a copy of the value of this variable.

experimental_ref()

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

static from_proto(variable_def, import_scope=None)

Returns a Variable object created from variable_def.

gather_nd(indices, name=None)

Gather slices from params into a Tensor with shape specified by indices.

See tf.gather_nd for details.

Parameters

• indices – A Tensor. Must be one of the following types: int32, int64. Index tensor.

• name – A name for the operation (optional).

Returns

A Tensor. Has the same type as params.

get_cache_deps(only_floating=True)

Return a set of all independent Parameter that this object depends on.

Parameters

only_floating (bool) – If True, only return floating Parameter

Return type

OrderedSet

get_dependencies(only_floating=True)

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use get_params instead if you want to retrieve the independent parameters or get_cache_deps in case you need the numerical cache dependents (advanced).

Return type

OrderedSet

get_params(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)

Recursively collect parameters that this object depends on according to the filter criteria.

Which parameters should be included can be steered using the arguments as a filter.

• None: do not filter on this. E.g. floating=None will return parameters that are floating as well as

  parameters that are fixed.

• True: only return parameters that fulfil this criterion

• False: only return parameters that do not fulfil this criterion. E.g. floating=False will return

  only parameters that are not floating.

Parameters

• floating (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True

• is_yield (Optional[bool]) – if a parameter is a yield of the _current_ model. This won’t be applied recursively, but may include yields if they do also represent a parameter parametrizing the shape. So if the yield of the current model depends on other yields (or also non-yields), this will be included. If, however, just submodels depend on a yield (as their yield) and it is not correlated to the output of our model, they won’t be included.

• extract_independent (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type

Set[ZfitParameter]

get_shape()

Alias of Variable.shape.

property graph

The Graph of this variable.

graph_caching_methods = []

property initial_value

Returns the Tensor used as the initial value for the variable.

Note that this is different from initialized_value() which runs the op that initializes the variable before returning its value. This method returns the tensor that is used by the op that initializes the variable.

Returns

A Tensor.

initialized_value()

Returns the value of the initialized variable. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.

You should use this instead of the variable itself to initialize another variable with a value that depends on the value of this variable.

`python # Initialize 'v' with a random tensor. v = tf.Variable(tf.random.truncated_normal([10, 40])) # Use `initialized_value` to guarantee that `v` has been # initialized before its value is used to initialize `w`. # The random values are picked only once. w = tf.Variable(v.initialized_value() * 2.0) `

Returns

A Tensor holding the value of this variable after its initializer has run.

property initializer

The initializer operation for this variable.

instances = <_weakrefset.WeakSet object>

load(value, session=None)

Load new value into this variable. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Variable.assign which has equivalent behavior in 2.X.

Writes new value to variable’s memory. Doesn’t add ops to the graph.

This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used. See tf.compat.v1.Session for more information on launching a graph and on sessions.

```python v = tf.Variable([1, 2]) init = tf.compat.v1.global_variables_initializer()

with tf.compat.v1.Session() as sess:

sess.run(init) # Usage passing the session explicitly. v.load([2, 3], sess) print(v.eval(sess)) # prints [2 3] # Usage with the default session. The ‘with’ block # above makes ‘sess’ the default session. v.load([3, 4], sess) print(v.eval()) # prints [3 4]

```

Parameters

• value – New variable value

• session – The session to use to evaluate this variable. If none, the default session is used.

Raises

ValueError – Session is not passed and no default session

property name

Return type

str

property op

The Operation of this variable.

property params

Return type

~ParametersType

ref()

Returns a hashable reference object to this Variable.

The primary use case for this API is to put variables in a set/dictionary. We can’t put variables in a set/dictionary as variable.__hash__() is no longer available starting Tensorflow 2.0.

The following will raise an exception starting 2.0

>>> x = tf.Variable(5)
>>> y = tf.Variable(10)
>>> z = tf.Variable(10)
>>> variable_set = {x, y, z}
Traceback (most recent call last):
  ...
TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
>>> variable_dict = {x: 'five', y: 'ten'}
Traceback (most recent call last):
  ...
TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.

Instead, we can use variable.ref().

>>> variable_set = {x.ref(), y.ref(), z.ref()}
>>> x.ref() in variable_set
True
>>> variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'}
>>> variable_dict[y.ref()]
'ten'

Also, the reference object provides .deref() function that returns the original Variable.

>>> x = tf.Variable(5)
>>> x.ref().deref()
<tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>

register_cacher(cacher)

Register a cacher that caches values produces by this instance; a dependent.

Parameters

cacher (Union[ForwardRef, Iterable[ForwardRef]]) –

reset_cache(reseter)

reset_cache_self()

Clear the cache of self and all dependent cachers.

scatter_add(sparse_delta, use_locking=False, name=None)

Adds tf.IndexedSlices to this variable.

Parameters

• sparse_delta – tf.IndexedSlices to be added to this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_div(sparse_delta, use_locking=False, name=None)

Divide this variable by tf.IndexedSlices.

Parameters

• sparse_delta – tf.IndexedSlices to divide this variable by.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_max(sparse_delta, use_locking=False, name=None)

Updates this variable with the max of tf.IndexedSlices and itself.

Parameters

• sparse_delta – tf.IndexedSlices to use as an argument of max with this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_min(sparse_delta, use_locking=False, name=None)

Updates this variable with the min of tf.IndexedSlices and itself.

Parameters

• sparse_delta – tf.IndexedSlices to use as an argument of min with this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_mul(sparse_delta, use_locking=False, name=None)

Multiply this variable by tf.IndexedSlices.

Parameters

• sparse_delta – tf.IndexedSlices to multiply this variable by.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_nd_add(indices, updates, name=None)

Applies sparse addition to individual values or slices in a Variable.

The Variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) add = v.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(add)

```

The resulting update to v would look like this:

[1, 13, 3, 14, 14, 6, 7, 20]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_nd_sub(indices, updates, name=None)

Applies sparse subtraction to individual values or slices in a Variable.

Assuming the variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = v.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

```

The resulting update to v would look like this:

[1, -9, 3, -6, -6, 6, 7, -4]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_nd_update(indices, updates, name=None)

Applies sparse assignment to individual values or slices in a Variable.

The Variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = v.scatter_nd_assign(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

```

The resulting update to v would look like this:

[1, 11, 3, 10, 9, 6, 7, 12]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_sub(sparse_delta, use_locking=False, name=None)

Subtracts tf.IndexedSlices from this variable.

Parameters

• sparse_delta – tf.IndexedSlices to be subtracted from this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_update(sparse_delta, use_locking=False, name=None)

Assigns tf.IndexedSlices to this variable.

Parameters

• sparse_delta – tf.IndexedSlices to be assigned to this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

set_shape(shape)

Overrides the shape for this variable.

Parameters

shape – the TensorShape representing the overridden shape.

sparse_read(indices, name=None)

Gather slices from params axis axis according to indices.

This function supports a subset of tf.gather, see tf.gather for details on usage.

Parameters

• indices – The index Tensor. Must be one of the following types: int32, int64. Must be in range [0, params.shape[axis]).

• name – A name for the operation (optional).

Returns

A Tensor. Has the same type as params.

property synchronization

to_proto(export_scope=None)

Converts a Variable to a VariableDef protocol buffer.

Parameters

export_scope – Optional string. Name scope to remove.

Returns

A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.

property trainable

class zfit.param.Parameter(name, value, lower_limit=None, upper_limit=None, step_size=None, floating=True, dtype=tf.float64, **kwargs)

Bases: zfit.core.parameter.ZfitParameterMixin, zfit.core.parameter.TFBaseVariable, zfit.core.parameter.BaseParameter, zfit.core.interfaces.ZfitIndependentParameter

Class for fit parameters, derived from TF Variable class.

Parameters

• name (str) – name of the parameter

• value (Union[int, float, complex, Tensor, ZfitParameter]) – starting value

• lower_limit (Union[int, float, complex, Tensor, ZfitParameter, None]) – lower limit

• upper_limit (Union[int, float, complex, Tensor, ZfitParameter, None]) – upper limit

• step_size (Union[int, float, complex, Tensor, ZfitParameter, None]) – step size

DEFAULT_STEP_SIZE = 0.001

property lower

property upper

property has_limits

If the parameter has limits set or not.

Return type

bool

property at_limit

If the value is at the limit (or over it).

The precision is up to 1e-5 relative.

Return type

Tensor

Returns

Boolean tf.Tensor that tells whether the value is at the limits.

value()

read_value()

property floating

property independent

property step_size

Step size of the parameter, the estimated order of magnitude of the uncertainty.

This can be crucial to tune for the minimization. A too large step_size can produce NaNs, a too small won’t converge.

If the step size is not set, the DEFAULT_STEP_SIZE is used.

Return type

Tensor

Returns

The step size

set_value(value)

Set the Parameter to value (temporarily if used in a context manager).

This operation won’t, compared to the assign, return the read value but an object that can act as a context manager.

Parameters

value (Union[int, float, complex, Tensor, ZfitParameter]) – The value the parameter will take on.

randomize(minval=None, maxval=None, sampler=<built-in method uniform of numpy.random.mtrand.RandomState object>)

Update the parameter with a randomised value between minval and maxval and return it.

Parameters

• minval (Union[int, float, complex, Tensor, ZfitParameter, None]) – The lower bound of the sampler. If not given, lower_limit is used.

• maxval (Union[int, float, complex, Tensor, ZfitParameter, None]) – The upper bound of the sampler. If not given, upper_limit is used.

• sampler (Callable) – A sampler with the same interface as np.random.uniform

Return type

Tensor

Returns

The sampled value

get_params(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)

Return type

Set[ZfitParameter]

property lower_limit

property upper_limit

class SaveSliceInfo(full_name=None, full_shape=None, var_offset=None, var_shape=None, save_slice_info_def=None, import_scope=None)

Bases: object

Information on how to save this Variable as a slice.

Provides internal support for saving variables as slices of a larger variable. This API is not public and is subject to change.

Available properties:

• full_name

• full_shape

• var_offset

• var_shape

Create a SaveSliceInfo.

Parameters

• full_name – Name of the full variable of which this Variable is a slice.

• full_shape – Shape of the full variable, as a list of int.

• var_offset – Offset of this Variable into the full variable, as a list of int.

• var_shape – Shape of this Variable, as a list of int.

• save_slice_info_def – SaveSliceInfoDef protocol buffer. If not None, recreates the SaveSliceInfo object its contents. save_slice_info_def and other arguments are mutually exclusive.

• import_scope – Optional string. Name scope to add. Only used when initializing from protocol buffer.

property spec

Computes the spec string used for saving.

to_proto(export_scope=None)

Returns a SaveSliceInfoDef() proto.

Parameters

export_scope – Optional string. Name scope to remove.

Returns

A SaveSliceInfoDef protocol buffer, or None if the Variable is not in the specified name scope.

__iter__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register __getitem__ as an overloaded operator, Python will valiantly attempt to iterate over the variable’s Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises

TypeError – when invoked.

__ne__(other)

Compares two variables element-wise for equality.

add_cache_deps(cache_deps, allow_non_cachable=True)

Add dependencies that render the cache invalid if they change.

Parameters

• cache_deps (Union[ForwardRef, Iterable[ForwardRef]]) –

• allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.

Raises

TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.

property aggregation

assign(value, use_locking=None, name=None, read_value=True)

Assigns a new value to this variable.

Parameters

• value – A Tensor. The new value for this variable.

• use_locking – If True, use locking during the assignment.

• name – The name to use for the assignment.

• read_value – A bool. Whether to read and return the new value of the variable or not.

Returns

If read_value is True, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the Operation that does the assignment, and when in eager mode it will return None.

assign_add(delta, use_locking=None, name=None, read_value=True)

Adds a value to this variable.

Parameters

• delta – A Tensor. The value to add to this variable.

• use_locking – If True, use locking during the operation.

• name – The name to use for the operation.

• read_value – A bool. Whether to read and return the new value of the variable or not.

Returns

If read_value is True, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the Operation that does the assignment, and when in eager mode it will return None.

assign_sub(delta, use_locking=None, name=None, read_value=True)

Subtracts a value from this variable.

Parameters

• delta – A Tensor. The value to subtract from this variable.

• use_locking – If True, use locking during the operation.

• name – The name to use for the operation.

• read_value – A bool. Whether to read and return the new value of the variable or not.

Returns

If read_value is True, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the Operation that does the assignment, and when in eager mode it will return None.

batch_scatter_update(sparse_delta, use_locking=False, name=None)

Assigns tf.IndexedSlices to this variable batch-wise.

Analogous to batch_gather. This assumes that this variable and the sparse_delta IndexedSlices have a series of leading dimensions that are the same for all of them, and the updates are performed on the last dimension of indices. In other words, the dimensions should be the following:

num_prefix_dims = sparse_delta.indices.ndims - 1 batch_dim = num_prefix_dims + 1 `sparse_delta.updates.shape = sparse_delta.indices.shape + var.shape[

batch_dim:]`

where

sparse_delta.updates.shape[:num_prefix_dims] == sparse_delta.indices.shape[:num_prefix_dims] == var.shape[:num_prefix_dims]

And the operation performed can be expressed as:

`var[i_1, …, i_n,

sparse_delta.indices[i_1, …, i_n, j]] = sparse_delta.updates[

i_1, …, i_n, j]`

When sparse_delta.indices is a 1D tensor, this operation is equivalent to scatter_update.

To avoid this operation one can looping over the first ndims of the variable and using scatter_update on the subtensors that result of slicing the first dimension. This is a valid option for ndims = 1, but less efficient than this implementation.

Parameters

• sparse_delta – tf.IndexedSlices to be assigned to this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

property constraint

Returns the constraint function associated with this variable.

Returns

The constraint function that was passed to the variable constructor. Can be None if no constraint was passed.

copy(deep=False, name=None, **overwrite_params)

Return type

ZfitObject

count_up_to(limit)

Increments this variable until it reaches limit. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Dataset.range instead.

When that Op is run it tries to increment the variable by 1. If incrementing the variable would bring it above limit then the Op raises the exception OutOfRangeError.

If no error is raised, the Op outputs the value of the variable before the increment.

This is essentially a shortcut for count_up_to(self, limit).

Parameters

limit – value at which incrementing the variable raises an error.

Returns

A Tensor that will hold the variable value before the increment. If no other Op modifies this variable, the values produced will all be distinct.

property create

The op responsible for initializing this variable.

property device

The device this variable is on.

property dtype

The dtype of the object

Return type

DType

eval(session=None)

Evaluates and returns the value of this variable.

experimental_ref()

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

static from_proto(variable_def, import_scope=None)

gather_nd(indices, name=None)

Reads the value of this variable sparsely, using gather_nd.

get_cache_deps(only_floating=True)

Return a set of all independent Parameter that this object depends on.

Parameters

only_floating (bool) – If True, only return floating Parameter

Return type

OrderedSet

get_dependencies(only_floating=True)

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use get_params instead if you want to retrieve the independent parameters or get_cache_deps in case you need the numerical cache dependents (advanced).

Return type

OrderedSet

get_shape()

Alias of Variable.shape.

property graph

The Graph of this variable.

graph_caching_methods = []

property handle

The handle by which this variable can be accessed.

property initial_value

Returns the Tensor used as the initial value for the variable.

initialized_value()

Returns the value of the initialized variable. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.

You should use this instead of the variable itself to initialize another variable with a value that depends on the value of this variable.

`python # Initialize 'v' with a random tensor. v = tf.Variable(tf.random.truncated_normal([10, 40])) # Use `initialized_value` to guarantee that `v` has been # initialized before its value is used to initialize `w`. # The random values are picked only once. w = tf.Variable(v.initialized_value() * 2.0) `

Returns

A Tensor holding the value of this variable after its initializer has run.

property initializer

The op responsible for initializing this variable.

instances = <_weakrefset.WeakSet object>

is_initialized(name=None)

Checks whether a resource variable has been initialized.

Outputs boolean scalar indicating whether the tensor has been initialized.

Parameters

name – A name for the operation (optional).

Returns

A Tensor of type bool.

load(value, session=None)

Load new value into this variable. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Variable.assign which has equivalent behavior in 2.X.

Writes new value to variable’s memory. Doesn’t add ops to the graph.

This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used. See tf.compat.v1.Session for more information on launching a graph and on sessions.

```python v = tf.Variable([1, 2]) init = tf.compat.v1.global_variables_initializer()

with tf.compat.v1.Session() as sess:

sess.run(init) # Usage passing the session explicitly. v.load([2, 3], sess) print(v.eval(sess)) # prints [2 3] # Usage with the default session. The ‘with’ block # above makes ‘sess’ the default session. v.load([3, 4], sess) print(v.eval()) # prints [3 4]

```

Parameters

• value – New variable value

• session – The session to use to evaluate this variable. If none, the default session is used.

Raises

ValueError – Session is not passed and no default session

property name

Return type

str

numpy()

property op

The op for this variable.

property params

Return type

~ParametersType

ref()

Returns a hashable reference object to this Variable.

The primary use case for this API is to put variables in a set/dictionary. We can’t put variables in a set/dictionary as variable.__hash__() is no longer available starting Tensorflow 2.0.

The following will raise an exception starting 2.0

>>> x = tf.Variable(5)
>>> y = tf.Variable(10)
>>> z = tf.Variable(10)
>>> variable_set = {x, y, z}
Traceback (most recent call last):
  ...
TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
>>> variable_dict = {x: 'five', y: 'ten'}
Traceback (most recent call last):
  ...
TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.

Instead, we can use variable.ref().

>>> variable_set = {x.ref(), y.ref(), z.ref()}
>>> x.ref() in variable_set
True
>>> variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'}
>>> variable_dict[y.ref()]
'ten'

Also, the reference object provides .deref() function that returns the original Variable.

>>> x = tf.Variable(5)
>>> x.ref().deref()
<tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>

register_cacher(cacher)

Register a cacher that caches values produces by this instance; a dependent.

Parameters

cacher (Union[ForwardRef, Iterable[ForwardRef]]) –

reset_cache(reseter)

reset_cache_self()

Clear the cache of self and all dependent cachers.

scatter_add(sparse_delta, use_locking=False, name=None)

Adds tf.IndexedSlices to this variable.

Parameters

• sparse_delta – tf.IndexedSlices to be added to this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_div(sparse_delta, use_locking=False, name=None)

Divide this variable by tf.IndexedSlices.

Parameters

• sparse_delta – tf.IndexedSlices to divide this variable by.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_max(sparse_delta, use_locking=False, name=None)

Updates this variable with the max of tf.IndexedSlices and itself.

Parameters

• sparse_delta – tf.IndexedSlices to use as an argument of max with this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_min(sparse_delta, use_locking=False, name=None)

Updates this variable with the min of tf.IndexedSlices and itself.

Parameters

• sparse_delta – tf.IndexedSlices to use as an argument of min with this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_mul(sparse_delta, use_locking=False, name=None)

Multiply this variable by tf.IndexedSlices.

Parameters

• sparse_delta – tf.IndexedSlices to multiply this variable by.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_nd_add(indices, updates, name=None)

Applies sparse addition to individual values or slices in a Variable.

ref is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the K`th dimension of `ref.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) add = ref.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(add)

```

The resulting update to ref would look like this:

[1, 13, 3, 14, 14, 6, 7, 20]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_nd_max(indices, updates, name=None)

Updates this variable with the max of tf.IndexedSlices and itself.

ref is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the K`th dimension of `ref.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_nd_min(indices, updates, name=None)

Updates this variable with the min of tf.IndexedSlices and itself.

ref is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the K`th dimension of `ref.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_nd_sub(indices, updates, name=None)

Applies sparse subtraction to individual values or slices in a Variable.

ref is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the K`th dimension of `ref.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = ref.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

```

The resulting update to ref would look like this:

[1, -9, 3, -6, -6, 6, 7, -4]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_nd_update(indices, updates, name=None)

Applies sparse assignment to individual values or slices in a Variable.

ref is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the K`th dimension of `ref.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = ref.scatter_nd_update(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

```

The resulting update to ref would look like this:

[1, 11, 3, 10, 9, 6, 7, 12]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_sub(sparse_delta, use_locking=False, name=None)

Subtracts tf.IndexedSlices from this variable.

Parameters

• sparse_delta – tf.IndexedSlices to be subtracted from this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_update(sparse_delta, use_locking=False, name=None)

Assigns tf.IndexedSlices to this variable.

Parameters

• sparse_delta – tf.IndexedSlices to be assigned to this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

set_shape(shape)

property shape

The shape of this variable.

sparse_read(indices, name=None)

Reads the value of this variable sparsely, using gather.

property synchronization

to_proto(export_scope=None)

Converts a ResourceVariable to a VariableDef protocol buffer.

Parameters

export_scope – Optional string. Name scope to remove.

Raises

RuntimeError – If run in EAGER mode.

Returns

A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.

property trainable

class zfit.param.ComposedParameter(name, value_fn, params=<class 'zfit.util.checks.NotSpecified'>, dtype=tf.float64, dependents=<class 'zfit.util.checks.NotSpecified'>)

Bases: zfit.core.parameter.BaseComposedParameter

Arbitrary composition of parameters.

A ComposedParameter allows for arbitrary combinations of parameters and correlations

Parameters

• name (str) – Unique name of the Parameter

• value_fn (Callable) – Function that returns the value of the composed parameter and takes as arguments params as arguments.

• params (Union[Dict[str, ZfitParameter], Iterable[ZfitParameter], ZfitParameter]) – If it is a dict, this will direclty be used as the params attribute, otherwise the parameters will be automatically named with f”param_{i}”. The values act as arguments to value_fn.

• dtype (DType) – Output of value_fn dtype

• dependents (Union[Dict[str, ZfitParameter], Iterable[ZfitParameter], ZfitParameter]) –

  Deprecated since version unknown: use params instead.

class SaveSliceInfo(full_name=None, full_shape=None, var_offset=None, var_shape=None, save_slice_info_def=None, import_scope=None)

Bases: object

Information on how to save this Variable as a slice.

Provides internal support for saving variables as slices of a larger variable. This API is not public and is subject to change.

Available properties:

• full_name

• full_shape

• var_offset

• var_shape

Create a SaveSliceInfo.

Parameters

• full_name – Name of the full variable of which this Variable is a slice.

• full_shape – Shape of the full variable, as a list of int.

• var_offset – Offset of this Variable into the full variable, as a list of int.

• var_shape – Shape of this Variable, as a list of int.

• save_slice_info_def – SaveSliceInfoDef protocol buffer. If not None, recreates the SaveSliceInfo object its contents. save_slice_info_def and other arguments are mutually exclusive.

• import_scope – Optional string. Name scope to add. Only used when initializing from protocol buffer.

property spec

Computes the spec string used for saving.

to_proto(export_scope=None)

Returns a SaveSliceInfoDef() proto.

Parameters

export_scope – Optional string. Name scope to remove.

Returns

A SaveSliceInfoDef protocol buffer, or None if the Variable is not in the specified name scope.

__iter__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register __getitem__ as an overloaded operator, Python will valiantly attempt to iterate over the variable’s Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises

TypeError – when invoked.

__ne__(other)

Compares two variables element-wise for equality.

add_cache_deps(cache_deps, allow_non_cachable=True)

Add dependencies that render the cache invalid if they change.

Parameters

• cache_deps (Union[ForwardRef, Iterable[ForwardRef]]) –

• allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.

Raises

TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.

property aggregation

assign(value, use_locking=False, name=None, read_value=True)

Assigns a new value to the variable.

This is essentially a shortcut for assign(self, value).

Parameters

• value – A Tensor. The new value for this variable.

• use_locking – If True, use locking during the assignment.

• name – The name of the operation to be created

• read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.

Returns

The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

assign_add(delta, use_locking=False, name=None, read_value=True)

Adds a value to this variable.

This is essentially a shortcut for assign_add(self, delta).

Parameters

• delta – A Tensor. The value to add to this variable.

• use_locking – If True, use locking during the operation.

• name – The name of the operation to be created

• read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.

Returns

The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

assign_sub(delta, use_locking=False, name=None, read_value=True)

Subtracts a value from this variable.

This is essentially a shortcut for assign_sub(self, delta).

Parameters

• delta – A Tensor. The value to subtract from this variable.

• use_locking – If True, use locking during the operation.

• name – The name of the operation to be created

• read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.

Returns

The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

batch_scatter_update(sparse_delta, use_locking=False, name=None)

Assigns tf.IndexedSlices to this variable batch-wise.

Analogous to batch_gather. This assumes that this variable and the sparse_delta IndexedSlices have a series of leading dimensions that are the same for all of them, and the updates are performed on the last dimension of indices. In other words, the dimensions should be the following:

num_prefix_dims = sparse_delta.indices.ndims - 1 batch_dim = num_prefix_dims + 1 `sparse_delta.updates.shape = sparse_delta.indices.shape + var.shape[

batch_dim:]`

where

sparse_delta.updates.shape[:num_prefix_dims] == sparse_delta.indices.shape[:num_prefix_dims] == var.shape[:num_prefix_dims]

And the operation performed can be expressed as:

`var[i_1, …, i_n,

sparse_delta.indices[i_1, …, i_n, j]] = sparse_delta.updates[

i_1, …, i_n, j]`

When sparse_delta.indices is a 1D tensor, this operation is equivalent to scatter_update.

To avoid this operation one can looping over the first ndims of the variable and using scatter_update on the subtensors that result of slicing the first dimension. This is a valid option for ndims = 1, but less efficient than this implementation.

Parameters

• sparse_delta – tf.IndexedSlices to be assigned to this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

property constraint

Returns the constraint function associated with this variable.

Returns

The constraint function that was passed to the variable constructor. Can be None if no constraint was passed.

copy(deep=False, name=None, **overwrite_params)

Return type

ZfitObject

count_up_to(limit)

Increments this variable until it reaches limit. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Dataset.range instead.

When that Op is run it tries to increment the variable by 1. If incrementing the variable would bring it above limit then the Op raises the exception OutOfRangeError.

If no error is raised, the Op outputs the value of the variable before the increment.

This is essentially a shortcut for count_up_to(self, limit).

Parameters

limit – value at which incrementing the variable raises an error.

Returns

A Tensor that will hold the variable value before the increment. If no other Op modifies this variable, the values produced will all be distinct.

property device

The device of this variable.

property dtype

The dtype of the object

Return type

DType

eval(session=None)

In a session, computes and returns the value of this variable.

This is not a graph construction method, it does not add ops to the graph.

This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used. See tf.compat.v1.Session for more information on launching a graph and on sessions.

```python v = tf.Variable([1, 2]) init = tf.compat.v1.global_variables_initializer()

with tf.compat.v1.Session() as sess:

sess.run(init) # Usage passing the session explicitly. print(v.eval(sess)) # Usage with the default session. The ‘with’ block # above makes ‘sess’ the default session. print(v.eval())

```

Parameters

session – The session to use to evaluate this variable. If none, the default session is used.

Returns

A numpy ndarray with a copy of the value of this variable.

experimental_ref()

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

property floating

static from_proto(variable_def, import_scope=None)

Returns a Variable object created from variable_def.

gather_nd(indices, name=None)

Gather slices from params into a Tensor with shape specified by indices.

See tf.gather_nd for details.

Parameters

• indices – A Tensor. Must be one of the following types: int32, int64. Index tensor.

• name – A name for the operation (optional).

Returns

A Tensor. Has the same type as params.

get_cache_deps(only_floating=True)

Return a set of all independent Parameter that this object depends on.

Parameters

only_floating (bool) – If True, only return floating Parameter

Return type

OrderedSet

get_dependencies(only_floating=True)

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use get_params instead if you want to retrieve the independent parameters or get_cache_deps in case you need the numerical cache dependents (advanced).

Return type

OrderedSet

get_params(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)

Recursively collect parameters that this object depends on according to the filter criteria.

Which parameters should be included can be steered using the arguments as a filter.

• None: do not filter on this. E.g. floating=None will return parameters that are floating as well as

  parameters that are fixed.

• True: only return parameters that fulfil this criterion

• False: only return parameters that do not fulfil this criterion. E.g. floating=False will return

  only parameters that are not floating.

Parameters

• floating (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True

• is_yield (Optional[bool]) – if a parameter is a yield of the _current_ model. This won’t be applied recursively, but may include yields if they do also represent a parameter parametrizing the shape. So if the yield of the current model depends on other yields (or also non-yields), this will be included. If, however, just submodels depend on a yield (as their yield) and it is not correlated to the output of our model, they won’t be included.

• extract_independent (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type

Set[ZfitParameter]

get_shape()

Alias of Variable.shape.

property graph

The Graph of this variable.

graph_caching_methods = []

property independent

property initial_value

Returns the Tensor used as the initial value for the variable.

Note that this is different from initialized_value() which runs the op that initializes the variable before returning its value. This method returns the tensor that is used by the op that initializes the variable.

Returns

A Tensor.

initialized_value()

Returns the value of the initialized variable. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.

You should use this instead of the variable itself to initialize another variable with a value that depends on the value of this variable.

`python # Initialize 'v' with a random tensor. v = tf.Variable(tf.random.truncated_normal([10, 40])) # Use `initialized_value` to guarantee that `v` has been # initialized before its value is used to initialize `w`. # The random values are picked only once. w = tf.Variable(v.initialized_value() * 2.0) `

Returns

A Tensor holding the value of this variable after its initializer has run.

property initializer

The initializer operation for this variable.

instances = <_weakrefset.WeakSet object>

load(value, session=None)

Load new value into this variable. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Variable.assign which has equivalent behavior in 2.X.

Writes new value to variable’s memory. Doesn’t add ops to the graph.

This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used. See tf.compat.v1.Session for more information on launching a graph and on sessions.

```python v = tf.Variable([1, 2]) init = tf.compat.v1.global_variables_initializer()

with tf.compat.v1.Session() as sess:

sess.run(init) # Usage passing the session explicitly. v.load([2, 3], sess) print(v.eval(sess)) # prints [2 3] # Usage with the default session. The ‘with’ block # above makes ‘sess’ the default session. v.load([3, 4], sess) print(v.eval()) # prints [3 4]

```

Parameters

• value – New variable value

• session – The session to use to evaluate this variable. If none, the default session is used.

Raises

ValueError – Session is not passed and no default session

property name

Return type

str

numpy()

property op

The Operation of this variable.

property params

read_value()

ref()

Returns a hashable reference object to this Variable.

The primary use case for this API is to put variables in a set/dictionary. We can’t put variables in a set/dictionary as variable.__hash__() is no longer available starting Tensorflow 2.0.

The following will raise an exception starting 2.0

>>> x = tf.Variable(5)
>>> y = tf.Variable(10)
>>> z = tf.Variable(10)
>>> variable_set = {x, y, z}
Traceback (most recent call last):
  ...
TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
>>> variable_dict = {x: 'five', y: 'ten'}
Traceback (most recent call last):
  ...
TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.

Instead, we can use variable.ref().

>>> variable_set = {x.ref(), y.ref(), z.ref()}
>>> x.ref() in variable_set
True
>>> variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'}
>>> variable_dict[y.ref()]
'ten'

Also, the reference object provides .deref() function that returns the original Variable.

>>> x = tf.Variable(5)
>>> x.ref().deref()
<tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>

register_cacher(cacher)

Register a cacher that caches values produces by this instance; a dependent.

Parameters

cacher (Union[ForwardRef, Iterable[ForwardRef]]) –

reset_cache(reseter)

reset_cache_self()

Clear the cache of self and all dependent cachers.

scatter_add(sparse_delta, use_locking=False, name=None)

Adds tf.IndexedSlices to this variable.

Parameters

• sparse_delta – tf.IndexedSlices to be added to this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_div(sparse_delta, use_locking=False, name=None)

Divide this variable by tf.IndexedSlices.

Parameters

• sparse_delta – tf.IndexedSlices to divide this variable by.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_max(sparse_delta, use_locking=False, name=None)

Updates this variable with the max of tf.IndexedSlices and itself.

Parameters

• sparse_delta – tf.IndexedSlices to use as an argument of max with this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_min(sparse_delta, use_locking=False, name=None)

Updates this variable with the min of tf.IndexedSlices and itself.

Parameters

• sparse_delta – tf.IndexedSlices to use as an argument of min with this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_mul(sparse_delta, use_locking=False, name=None)

Multiply this variable by tf.IndexedSlices.

Parameters

• sparse_delta – tf.IndexedSlices to multiply this variable by.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_nd_add(indices, updates, name=None)

Applies sparse addition to individual values or slices in a Variable.

The Variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) add = v.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(add)

```

The resulting update to v would look like this:

[1, 13, 3, 14, 14, 6, 7, 20]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_nd_sub(indices, updates, name=None)

Applies sparse subtraction to individual values or slices in a Variable.

Assuming the variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = v.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

```

The resulting update to v would look like this:

[1, -9, 3, -6, -6, 6, 7, -4]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_nd_update(indices, updates, name=None)

Applies sparse assignment to individual values or slices in a Variable.

The Variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = v.scatter_nd_assign(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

```

The resulting update to v would look like this:

[1, 11, 3, 10, 9, 6, 7, 12]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_sub(sparse_delta, use_locking=False, name=None)

Subtracts tf.IndexedSlices from this variable.

Parameters

• sparse_delta – tf.IndexedSlices to be subtracted from this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_update(sparse_delta, use_locking=False, name=None)

Assigns tf.IndexedSlices to this variable.

Parameters

• sparse_delta – tf.IndexedSlices to be assigned to this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

set_shape(shape)

Overrides the shape for this variable.

Parameters

shape – the TensorShape representing the overridden shape.

property shape

sparse_read(indices, name=None)

Gather slices from params axis axis according to indices.

This function supports a subset of tf.gather, see tf.gather for details on usage.

Parameters

• indices – The index Tensor. Must be one of the following types: int32, int64. Must be in range [0, params.shape[axis]).

• name – A name for the operation (optional).

Returns

A Tensor. Has the same type as params.

property synchronization

to_proto(export_scope=None)

Converts a Variable to a VariableDef protocol buffer.

Parameters

export_scope – Optional string. Name scope to remove.

Returns

A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.

property trainable

value()

class zfit.param.ComplexParameter(*args, **kwargs)

Bases: zfit.core.parameter.ComposedParameter

Create a complex parameter.

Note

Use the constructor class methods instead of the __init__() constructor:

• ComplexParameter.from_cartesian()

• ComplexParameter.from_polar()

class SaveSliceInfo(full_name=None, full_shape=None, var_offset=None, var_shape=None, save_slice_info_def=None, import_scope=None)

Bases: object

Information on how to save this Variable as a slice.

Provides internal support for saving variables as slices of a larger variable. This API is not public and is subject to change.

Available properties:

• full_name

• full_shape

• var_offset

• var_shape

Create a SaveSliceInfo.

Parameters

• full_name – Name of the full variable of which this Variable is a slice.

• full_shape – Shape of the full variable, as a list of int.

• var_offset – Offset of this Variable into the full variable, as a list of int.

• var_shape – Shape of this Variable, as a list of int.

• save_slice_info_def – SaveSliceInfoDef protocol buffer. If not None, recreates the SaveSliceInfo object its contents. save_slice_info_def and other arguments are mutually exclusive.

• import_scope – Optional string. Name scope to add. Only used when initializing from protocol buffer.

property spec

Computes the spec string used for saving.

to_proto(export_scope=None)

Returns a SaveSliceInfoDef() proto.

Parameters

export_scope – Optional string. Name scope to remove.

Returns

A SaveSliceInfoDef protocol buffer, or None if the Variable is not in the specified name scope.

__iter__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register __getitem__ as an overloaded operator, Python will valiantly attempt to iterate over the variable’s Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises

TypeError – when invoked.

__ne__(other)

Compares two variables element-wise for equality.

add_cache_deps(cache_deps, allow_non_cachable=True)

Add dependencies that render the cache invalid if they change.

Parameters

• cache_deps (Union[ForwardRef, Iterable[ForwardRef]]) –

• allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.

Raises

TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.

property aggregation

assign(value, use_locking=False, name=None, read_value=True)

Assigns a new value to the variable.

This is essentially a shortcut for assign(self, value).

Parameters

• value – A Tensor. The new value for this variable.

• use_locking – If True, use locking during the assignment.

• name – The name of the operation to be created

• read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.

Returns

The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

assign_add(delta, use_locking=False, name=None, read_value=True)

Adds a value to this variable.

This is essentially a shortcut for assign_add(self, delta).

Parameters

• delta – A Tensor. The value to add to this variable.

• use_locking – If True, use locking during the operation.

• name – The name of the operation to be created

• read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.

Returns

The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

assign_sub(delta, use_locking=False, name=None, read_value=True)

Subtracts a value from this variable.

This is essentially a shortcut for assign_sub(self, delta).

Parameters

• delta – A Tensor. The value to subtract from this variable.

• use_locking – If True, use locking during the operation.

• name – The name of the operation to be created

• read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.

Returns

The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

batch_scatter_update(sparse_delta, use_locking=False, name=None)

Assigns tf.IndexedSlices to this variable batch-wise.

Analogous to batch_gather. This assumes that this variable and the sparse_delta IndexedSlices have a series of leading dimensions that are the same for all of them, and the updates are performed on the last dimension of indices. In other words, the dimensions should be the following:

num_prefix_dims = sparse_delta.indices.ndims - 1 batch_dim = num_prefix_dims + 1 `sparse_delta.updates.shape = sparse_delta.indices.shape + var.shape[

batch_dim:]`

where

sparse_delta.updates.shape[:num_prefix_dims] == sparse_delta.indices.shape[:num_prefix_dims] == var.shape[:num_prefix_dims]

And the operation performed can be expressed as:

`var[i_1, …, i_n,

sparse_delta.indices[i_1, …, i_n, j]] = sparse_delta.updates[

i_1, …, i_n, j]`

When sparse_delta.indices is a 1D tensor, this operation is equivalent to scatter_update.

To avoid this operation one can looping over the first ndims of the variable and using scatter_update on the subtensors that result of slicing the first dimension. This is a valid option for ndims = 1, but less efficient than this implementation.

Parameters

• sparse_delta – tf.IndexedSlices to be assigned to this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

property constraint

Returns the constraint function associated with this variable.

Returns

The constraint function that was passed to the variable constructor. Can be None if no constraint was passed.

copy(deep=False, name=None, **overwrite_params)

Return type

ZfitObject

count_up_to(limit)

Increments this variable until it reaches limit. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Dataset.range instead.

When that Op is run it tries to increment the variable by 1. If incrementing the variable would bring it above limit then the Op raises the exception OutOfRangeError.

If no error is raised, the Op outputs the value of the variable before the increment.

This is essentially a shortcut for count_up_to(self, limit).

Parameters

limit – value at which incrementing the variable raises an error.

Returns

A Tensor that will hold the variable value before the increment. If no other Op modifies this variable, the values produced will all be distinct.

property device

The device of this variable.

property dtype

The dtype of the object

Return type

DType

eval(session=None)

In a session, computes and returns the value of this variable.

This is not a graph construction method, it does not add ops to the graph.

This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used. See tf.compat.v1.Session for more information on launching a graph and on sessions.

```python v = tf.Variable([1, 2]) init = tf.compat.v1.global_variables_initializer()

with tf.compat.v1.Session() as sess:

sess.run(init) # Usage passing the session explicitly. print(v.eval(sess)) # Usage with the default session. The ‘with’ block # above makes ‘sess’ the default session. print(v.eval())

```

Parameters

session – The session to use to evaluate this variable. If none, the default session is used.

Returns

A numpy ndarray with a copy of the value of this variable.

experimental_ref()

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

property floating

static from_proto(variable_def, import_scope=None)

Returns a Variable object created from variable_def.

gather_nd(indices, name=None)

Gather slices from params into a Tensor with shape specified by indices.

See tf.gather_nd for details.

Parameters

• indices – A Tensor. Must be one of the following types: int32, int64. Index tensor.

• name – A name for the operation (optional).

Returns

A Tensor. Has the same type as params.

get_cache_deps(only_floating=True)

Return a set of all independent Parameter that this object depends on.

Parameters

only_floating (bool) – If True, only return floating Parameter

Return type

OrderedSet

get_dependencies(only_floating=True)

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use get_params instead if you want to retrieve the independent parameters or get_cache_deps in case you need the numerical cache dependents (advanced).

Return type

OrderedSet

get_params(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)

Recursively collect parameters that this object depends on according to the filter criteria.

Which parameters should be included can be steered using the arguments as a filter.

• None: do not filter on this. E.g. floating=None will return parameters that are floating as well as

  parameters that are fixed.

• True: only return parameters that fulfil this criterion

• False: only return parameters that do not fulfil this criterion. E.g. floating=False will return

  only parameters that are not floating.

Parameters

• floating (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True

• is_yield (Optional[bool]) – if a parameter is a yield of the _current_ model. This won’t be applied recursively, but may include yields if they do also represent a parameter parametrizing the shape. So if the yield of the current model depends on other yields (or also non-yields), this will be included. If, however, just submodels depend on a yield (as their yield) and it is not correlated to the output of our model, they won’t be included.

• extract_independent (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type

Set[ZfitParameter]

get_shape()

Alias of Variable.shape.

property graph

The Graph of this variable.

graph_caching_methods = []

property independent

property initial_value

Returns the Tensor used as the initial value for the variable.

Note that this is different from initialized_value() which runs the op that initializes the variable before returning its value. This method returns the tensor that is used by the op that initializes the variable.

Returns

A Tensor.

initialized_value()

Returns the value of the initialized variable. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.

You should use this instead of the variable itself to initialize another variable with a value that depends on the value of this variable.

`python # Initialize 'v' with a random tensor. v = tf.Variable(tf.random.truncated_normal([10, 40])) # Use `initialized_value` to guarantee that `v` has been # initialized before its value is used to initialize `w`. # The random values are picked only once. w = tf.Variable(v.initialized_value() * 2.0) `

Returns

A Tensor holding the value of this variable after its initializer has run.

property initializer

The initializer operation for this variable.

instances = <_weakrefset.WeakSet object>

load(value, session=None)

Load new value into this variable. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Variable.assign which has equivalent behavior in 2.X.

Writes new value to variable’s memory. Doesn’t add ops to the graph.

This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used. See tf.compat.v1.Session for more information on launching a graph and on sessions.

```python v = tf.Variable([1, 2]) init = tf.compat.v1.global_variables_initializer()

with tf.compat.v1.Session() as sess:

sess.run(init) # Usage passing the session explicitly. v.load([2, 3], sess) print(v.eval(sess)) # prints [2 3] # Usage with the default session. The ‘with’ block # above makes ‘sess’ the default session. v.load([3, 4], sess) print(v.eval()) # prints [3 4]

```

Parameters

• value – New variable value

• session – The session to use to evaluate this variable. If none, the default session is used.

Raises

ValueError – Session is not passed and no default session

property name

Return type

str

numpy()

property op

The Operation of this variable.

property params

read_value()

ref()

Returns a hashable reference object to this Variable.

The primary use case for this API is to put variables in a set/dictionary. We can’t put variables in a set/dictionary as variable.__hash__() is no longer available starting Tensorflow 2.0.

The following will raise an exception starting 2.0

>>> x = tf.Variable(5)
>>> y = tf.Variable(10)
>>> z = tf.Variable(10)
>>> variable_set = {x, y, z}
Traceback (most recent call last):
  ...
TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
>>> variable_dict = {x: 'five', y: 'ten'}
Traceback (most recent call last):
  ...
TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.

Instead, we can use variable.ref().

>>> variable_set = {x.ref(), y.ref(), z.ref()}
>>> x.ref() in variable_set
True
>>> variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'}
>>> variable_dict[y.ref()]
'ten'

Also, the reference object provides .deref() function that returns the original Variable.

>>> x = tf.Variable(5)
>>> x.ref().deref()
<tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>

register_cacher(cacher)

Register a cacher that caches values produces by this instance; a dependent.

Parameters

cacher (Union[ForwardRef, Iterable[ForwardRef]]) –

reset_cache(reseter)

reset_cache_self()

Clear the cache of self and all dependent cachers.

scatter_add(sparse_delta, use_locking=False, name=None)

Adds tf.IndexedSlices to this variable.

Parameters

• sparse_delta – tf.IndexedSlices to be added to this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_div(sparse_delta, use_locking=False, name=None)

Divide this variable by tf.IndexedSlices.

Parameters

• sparse_delta – tf.IndexedSlices to divide this variable by.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_max(sparse_delta, use_locking=False, name=None)

Updates this variable with the max of tf.IndexedSlices and itself.

Parameters

• sparse_delta – tf.IndexedSlices to use as an argument of max with this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_min(sparse_delta, use_locking=False, name=None)

Updates this variable with the min of tf.IndexedSlices and itself.

Parameters

• sparse_delta – tf.IndexedSlices to use as an argument of min with this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_mul(sparse_delta, use_locking=False, name=None)

Multiply this variable by tf.IndexedSlices.

Parameters

• sparse_delta – tf.IndexedSlices to multiply this variable by.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_nd_add(indices, updates, name=None)

Applies sparse addition to individual values or slices in a Variable.

The Variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) add = v.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(add)

```

The resulting update to v would look like this:

[1, 13, 3, 14, 14, 6, 7, 20]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_nd_sub(indices, updates, name=None)

Applies sparse subtraction to individual values or slices in a Variable.

Assuming the variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = v.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

```

The resulting update to v would look like this:

[1, -9, 3, -6, -6, 6, 7, -4]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_nd_update(indices, updates, name=None)

Applies sparse assignment to individual values or slices in a Variable.

The Variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the `K`th dimension of self.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = v.scatter_nd_assign(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

```

The resulting update to v would look like this:

[1, 11, 3, 10, 9, 6, 7, 12]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters

• indices – The indices to be used in the operation.

• updates – The values to be used in the operation.

• name – the name of the operation.

Returns

The updated variable.

scatter_sub(sparse_delta, use_locking=False, name=None)

Subtracts tf.IndexedSlices from this variable.

Parameters

• sparse_delta – tf.IndexedSlices to be subtracted from this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_update(sparse_delta, use_locking=False, name=None)

Assigns tf.IndexedSlices to this variable.

Parameters

• sparse_delta – tf.IndexedSlices to be assigned to this variable.

• use_locking – If True, use locking during the operation.

• name – the name of the operation.

Returns

The updated variable.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

set_shape(shape)

Overrides the shape for this variable.

Parameters

shape – the TensorShape representing the overridden shape.

property shape

sparse_read(indices, name=None)

Gather slices from params axis axis according to indices.

This function supports a subset of tf.gather, see tf.gather for details on usage.

Parameters

• indices – The index Tensor. Must be one of the following types: int32, int64. Must be in range [0, params.shape[axis]).

• name – A name for the operation (optional).

Returns

A Tensor. Has the same type as params.

property synchronization

to_proto(export_scope=None)

Converts a Variable to a VariableDef protocol buffer.

Parameters

export_scope – Optional string. Name scope to remove.

Returns

A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.

property trainable

value()

classmethod from_cartesian(name, real, imag, dtype=tf.complex128, floating=True)

Create a complex parameter from cartesian coordinates.

Parameters

• name – Name of the parameter.

• real – Real part of the complex number.

• imag – Imaginary part of the complex number.

classmethod from_polar(name, mod, arg, dtype=tf.complex128, floating=True, **kwargs)

Create a complex parameter from polar coordinates.

Parameters

• name – Name of the parameter.

• real – Modulus (r) the complex number.

• imag – Argument (phi) of the complex number.

property conj

Returns a complex conjugated copy of the complex parameter.

property real

Real part of the complex parameter.

property imag

Imaginary part of the complex parameter.

property mod

Modulus (r) of the complex parameter.

property arg

Argument (phi) of the complex parameter.

zfit.param.convert_to_parameter(value, name=None, prefer_constant=True, dependents=None)

Convert a numerical to a constant/floating parameter or return if already a parameter.

Parameters

• value –

• name –

• prefer_constant – If True, create a ConstantParameter instead of a Parameter, if possible.

Return type

ZfitParameter

zfit.param.set_values(params, values)

Set the values (using a context manager or not) of multiple parameters.

Parameters

• params (Union[Parameter, Iterable[Parameter]]) – Parameters to set the values.

• values (Union[int, float, complex, Tensor, ZfitParameter, Iterable[Union[int, float, complex, Tensor, ZfitParameter]], ZfitResult]) – List-like object that supports indexing.

Returns: