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Commit 6137a6f

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mlloreda9prady9
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Documented statistics API.
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‎arrayfire/statistics.py‎

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@@ -15,6 +15,27 @@
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from .array import *
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def mean(a, weights=None, dim=None):
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"""
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Calculate mean along a given dimension.
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Parameters
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----------
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a: af.Array
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The input array.
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weights: optional: af.Array. default: None.
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Array to calculate the weighted mean. Must match size of the
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input array.
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dim: optional: int. default: None.
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The dimension for which to obtain the mean from input data.
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Returns
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-------
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output: af.Array
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Array containing the mean of the input array along a given
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dimension.
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"""
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if dim is not None:
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out = Array()
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@@ -39,6 +60,31 @@ def mean(a, weights=None, dim=None):
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return real if imag == 0 else real + imag * 1j
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def var(a, isbiased=False, weights=None, dim=None):
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"""
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Calculate variance along a given dimension.
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Parameters
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----------
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a: af.Array
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The input array.
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isbiased: optional: Boolean. default: False.
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Boolean denoting population variance (false) or sample
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variance (true).
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weights: optional: af.Array. default: None.
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Array to calculate for the weighted mean. Must match size of
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the input array.
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dim: optional: int. default: None.
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The dimension for which to obtain the variance from input data.
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Returns
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-------
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output: af.Array
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Array containing the variance of the input array along a given
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dimension.
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"""
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if dim is not None:
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out = Array()
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@@ -63,6 +109,24 @@ def var(a, isbiased=False, weights=None, dim=None):
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return real if imag == 0 else real + imag * 1j
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def stdev(a, dim=None):
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"""
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Calculate standard deviation along a given dimension.
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Parameters
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----------
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a: af.Array
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The input array.
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dim: optional: int. default: None.
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The dimension for which to obtain the standard deviation from
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input data.
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Returns
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-------
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output: af.Array
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Array containing the standard deviation of the input array
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along a given dimension.
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"""
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if dim is not None:
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out = Array()
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safe_call(backend.get().af_stdev(c_pointer(out.arr), a.arr, c_int_t(dim)))
@@ -76,6 +140,26 @@ def stdev(a, dim=None):
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return real if imag == 0 else real + imag * 1j
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def cov(a, isbiased=False, dim=None):
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"""
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Calculate covariance along a given dimension.
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Parameters
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----------
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a: af.Array
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The input array.
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isbiased: optional: Boolean. default: False.
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Boolean denoting whether biased estimate should be taken.
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dim: optional: int. default: None.
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The dimension for which to obtain the covariance from input data.
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Returns
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-------
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output: af.Array
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Array containing the covariance of the input array along a
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given dimension.
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"""
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if dim is not None:
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out = Array()
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safe_call(backend.get().af_cov(c_pointer(out.arr), a.arr, isbiased, c_int_t(dim)))
@@ -89,6 +173,23 @@ def cov(a, isbiased=False, dim=None):
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return real if imag == 0 else real + imag * 1j
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def median(a, dim=None):
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"""
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Calculate median along a given dimension.
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Parameters
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----------
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a: af.Array
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The input array.
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dim: optional: int. default: None.
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The dimension for which to obtain the median from input data.
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Returns
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-------
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output: af.Array
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Array containing the median of the input array along a
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given dimension.
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"""
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if dim is not None:
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out = Array()
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safe_call(backend.get().af_median(c_pointer(out.arr), a.arr, c_int_t(dim)))
@@ -102,6 +203,22 @@ def median(a, dim=None):
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return real if imag == 0 else real + imag * 1j
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def corrcoef(x, y):
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"""
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Calculate the correlation coefficient of the input arrays.
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Parameters
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----------
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x: af.Array
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The first input array.
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y: af.Array
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The second input array.
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Returns
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-------
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output: af.Array
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Array containing the correlation coefficient of the input arrays.
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"""
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real = c_double_t(0)
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imag = c_double_t(0)
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safe_call(backend.get().af_corrcoef(c_pointer(real), c_pointer(imag), x.arr, y.arr))

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