arrayfire.algorithm module¶
Vector algorithms (sum, min, sort, etc).
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arrayfire.algorithm.accum(a, dim=0)[source]¶ Cumulative sum of an array along a specified dimension
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: 0
Dimension along which the cumulative sum is required.
Returns: out: af.Array
array of same size as a containing the cumulative sum along dim.
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arrayfire.algorithm.all_true(a, dim=None)[source]¶ Check if all the elements along a specified dimension are true.
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: None
Dimension along which the product is required.
Returns: out: af.Array or scalar number
Af.array containing True if all elements in a along the dimension are True. If dim is None, output is True if a does not have any zeros, else False.
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arrayfire.algorithm.any_true(a, dim=None)[source]¶ Check if any the elements along a specified dimension are true.
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: None
Dimension along which the product is required.
Returns: out: af.Array or scalar number
Af.array containing True if any elements in a along the dimension are True. If dim is None, output is True if a does not have any zeros, else False.
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arrayfire.algorithm.count(a, dim=None)[source]¶ Count the number of non zero elements in an array along a specified dimension.
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: None
Dimension along which the the non zero elements are to be counted.
Returns: out: af.Array or scalar number
The count of non zero elements in a along dim. If dim is None, the total number of non zero elements in a.
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arrayfire.algorithm.diff1(a, dim=0)[source]¶ Find the first order differences along specified dimensions
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: 0
Dimension along which the differences are required.
Returns: out: af.Array
Array whose length along dim is 1 less than that of a.
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arrayfire.algorithm.diff2(a, dim=0)[source]¶ Find the second order differences along specified dimensions
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: 0
Dimension along which the differences are required.
Returns: out: af.Array
Array whose length along dim is 2 less than that of a.
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arrayfire.algorithm.imax(a, dim=None)[source]¶ Find the value and location of the maximum value along a specified dimension
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: None
Dimension along which the maximum value is required.
Returns: (val, idx): tuple of af.Array or scalars
val contains the maximum value of a along dim. idx contains the location of where val occurs in a along dim. If dim is None, val and idx value and location of global maximum.
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arrayfire.algorithm.imin(a, dim=None)[source]¶ Find the value and location of the minimum value along a specified dimension
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: None
Dimension along which the minimum value is required.
Returns: (val, idx): tuple of af.Array or scalars
val contains the minimum value of a along dim. idx contains the location of where val occurs in a along dim. If dim is None, val and idx value and location of global minimum.
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arrayfire.algorithm.max(a, dim=None)[source]¶ Find the maximum value of all the elements along a specified dimension.
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: None
Dimension along which the maximum value is required.
Returns: out: af.Array or scalar number
The maximum value of all elements in a along dimension dim. If dim is None, maximum value of the entire Array is returned.
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arrayfire.algorithm.min(a, dim=None)[source]¶ Find the minimum value of all the elements along a specified dimension.
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: None
Dimension along which the minimum value is required.
Returns: out: af.Array or scalar number
The minimum value of all elements in a along dimension dim. If dim is None, minimum value of the entire Array is returned.
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arrayfire.algorithm.product(a, dim=None, nan_val=None)[source]¶ Calculate the product of all the elements along a specified dimension.
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: None
Dimension along which the product is required.
nan_val: optional: scalar. default: None
The value that replaces NaN in the array
Returns: out: af.Array or scalar number
The product of all elements in a along dimension dim. If dim is None, product of the entire Array is returned.
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arrayfire.algorithm.set_intersect(a, b, is_unique=False)[source]¶ Find the intersect of two arrays.
Parameters: a : af.Array
A 1D arrayfire array.
b : af.Array
A 1D arrayfire array.
is_unique: optional: bool. default: False
Specifies if the both inputs contain unique elements.
Returns: out: af.Array
an array values after performing the intersect of a and b.
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arrayfire.algorithm.set_union(a, b, is_unique=False)[source]¶ Find the union of two arrays.
Parameters: a : af.Array
A 1D arrayfire array.
b : af.Array
A 1D arrayfire array.
is_unique: optional: bool. default: False
Specifies if the both inputs contain unique elements.
Returns: out: af.Array
an array values after performing the union of a and b.
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arrayfire.algorithm.set_unique(a, is_sorted=False)[source]¶ Find the unique elements of an array.
Parameters: a : af.Array
A 1D arrayfire array.
is_sorted: optional: bool. default: False
Specifies if the input is pre-sorted.
Returns: out: af.Array
an array containing the unique values from a
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arrayfire.algorithm.sort(a, dim=0, is_ascending=True)[source]¶ Sort the array along a specified dimension
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: 0
Dimension along which sort is to be performed.
is_ascending: optional: bool. default: True
Specifies the direction of the sort
Returns: out: af.Array
array containing the sorted values
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arrayfire.algorithm.sort_by_key(iv, ik, dim=0, is_ascending=True)[source]¶ Sort an array based on specified keys
Parameters: iv : af.Array
An Array containing the values
ik : af.Array
An Array containing the keys
dim: optional: int. default: 0
Dimension along which sort is to be performed.
is_ascending: optional: bool. default: True
Specifies the direction of the sort
Returns: (ov, ok): tuple of af.Array
ov contains the values from iv after sorting them based on ik ok contains the values from ik in sorted order
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arrayfire.algorithm.sort_index(a, dim=0, is_ascending=True)[source]¶ Sort the array along a specified dimension and get the indices.
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: 0
Dimension along which sort is to be performed.
is_ascending: optional: bool. default: True
Specifies the direction of the sort
Returns: (val, idx): tuple of af.Array
val is an af.Array containing the sorted values. idx is an af.Array containing the original indices of val in a.
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arrayfire.algorithm.sum(a, dim=None, nan_val=None)[source]¶ Calculate the sum of all the elements along a specified dimension.
Parameters: a : af.Array
Multi dimensional arrayfire array.
dim: optional: int. default: None
Dimension along which the sum is required.
nan_val: optional: scalar. default: None
The value that replaces NaN in the array
Returns: out: af.Array or scalar number
The sum of all elements in a along dimension dim. If dim is None, sum of the entire Array is returned.
