Package pycv :: Package cs :: Package stats

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Package stats

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An umbrella package containing all packages related to Probability and Statistics.

Submodules

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pycv.cs.stats.gaussian: An umbrella package containing all packages related to Gaussian distribution assumptions.
- pycv.cs.stats.gaussian.gaussian
- pycv.cs.stats.gaussian.info: An umbrella package containing all packages related to Gaussian distribution assumptions.
pycv.cs.stats.info: An umbrella package containing all packages related to Probability and Statistics.
pycv.cs.stats.stats

Classes

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BasicStats
A few statistics of a random tensor (RT) are packed as a tuple.

Stats2
a total weight

Stats2e
Statistics of a random tensor (RT) up to 2nd-order are packed as a tuple.

Stats2m
a total weight

Functions

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statslist(data, weights=None, masks=None)
Return the (weighted) total weight, mean, and covariance of the data

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weighted(w, A)
Compute w[0]*A[0] + w[1]*A[1] + ...

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moment2e(data, weights=None)
Return the per-element 2nd order (weighted) moment of the data

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stde(n, s2e, mean)
Get the per-element standard deviation tensor based on 0th and per-element 2nd order moments and mean

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sum_outer(A, B=None)
Compute outer(A[0],B[0]) + outer(A[1],B[1]) + ...

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moment11(data1, data2, weights=None)
Return the (1,1)-th order (weighted) cross moment the two data (point1 * point2^T)

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statsliste(data, weights=None, masks=None)
Return the per-element (weighted) total weight, means, and standard deviations of the data

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moment2(data, weights=None)
Return the 2nd order (weighted) moment of the data

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moment1(data, weights=None)
Return the 1st order (weighted) moment of the data

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moment0(data, weights=None)
Return the 0th order (weighted) moment of the data

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covariance(n, s2, mean)
Get the covariance tensor based on 0th and 2nd order moments and mean

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weighted_outer(w, A, B=None)
Compute w[0]*outer(A[0],B[0]) + w[1]*outer(A[1],B[1]) + ...

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correlation(n, s11, mean1, mean2)
Compute the correlation of 2 datasets based on their 0th and (1,1)-th order cross moment and their mean tensors.

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mean(n, s1)
Get the mean tensor based on 0th and 1st order moments

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Variables

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__doc__

stdnorm = <scipy.stats.distributions.rv_frozen object at 0x03A...

Function Details

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statslist(data, weights=None, masks=None)

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Return the (weighted) total weight, mean, and covariance of the data

Input:: data: a numpy.array weights: an array of weights for the instances [optional] masks: an array of masks for the instances [optional]
Output:: n: total weight mean: mean tensor thecov: covariance tensor

weighted(w, A)

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Compute w[0]*A[0] + w[1]*A[1] + ... until the end.

Input:: w: a 1D numpy.array A: a multi-dimensional numpy.array

moment2e(data, weights=None)

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Return the per-element 2nd order (weighted) moment of the data

Input:: data: a numpy.array weights: an array of weights for the instances [optional]
Output:: s2e: per-element 2nd order (weighted) moment

stde(n, s2e, mean)

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Get the per-element standard deviation tensor based on 0th and per-element 2nd order moments and mean

Input:: n: 0th order moment s2e: per-element 2nd order moment -- see moment2e() mean: mean temsor -- see mean()
Output:: stde: per-element stdandard deviation tensor

sum_outer(A, B=None)

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Compute outer(A[0],B[0]) + outer(A[1],B[1]) + ... until the end.

Input:: A: a multi-dimensional numpy.array B: a multi-dimensional numpy.array (optional)

moment11(data1, data2, weights=None)

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Return the (1,1)-th order (weighted) cross moment the two data (point1 * point2^T)

Input:: data1: a numpy.array data2: a numpy.array weights: an array of weights for the instances [optional]
Output:: s11: (1,1)-th order (weighted) cross moment

statsliste(data, weights=None, masks=None)

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Return the per-element (weighted) total weight, means, and standard deviations of the data

Input:: data: a numpy.array weights: an array of weights for the instances [optional] masks: an array of masks for the instances [optional]
Output:: n: total weight mean: mean tensor thestd: standard deviation tensor

moment2(data, weights=None)

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Return the 2nd order (weighted) moment of the data

Input:: data: a numpy.array weights: an array of weights for the instances [optional]
Output:: s2: 2nd order (weighted) moment

moment1(data, weights=None)

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Return the 1st order (weighted) moment of the data

Input:: data: a numpy.array weights: an array of weights for the instances [optional]
Output:: s1: 1st order (weighted) moment

moment0(data, weights=None)

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Return the 0th order (weighted) moment of the data

Input:: data: a numpy.array weights: an array of weights for the instances [optional]
Output:: 0th order moment -- number of (weighted) instances -- equivalent to len(data)

covariance(n, s2, mean)

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Get the covariance tensor based on 0th and 2nd order moments and mean

Input:: n: 0th order moment s2: 2nd order moment mean: mean tensor -- see mean()
Output:: thecov: covariance tensor

weighted_outer(w, A, B=None)

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Compute w[0]*outer(A[0],B[0]) + w[1]*outer(A[1],B[1]) + ... until the end.

Input:: w: a 1D numpy.array A: a multi-dimensional numpy.array B: a multi-dimensional numpy.array (optional)

correlation(n, s11, mean1, mean2)

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Compute the correlation of 2 datasets based on their 0th and (1,1)-th order cross moment and their mean tensors.

Input:: n: 0th order moment s12: cross moment of the two datasets -- see moment11() mean1: mean tensor of the first dataset -- see mean() mean2: mean tensor of the second dataset -- see mean()
Output:: cor: correlation tensor of the two dataset (point1*point2^T)

mean(n, s1)

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Get the mean tensor based on 0th and 1st order moments

Input:: n: 0th order moment s1: 1st order moment
Output:: mean: mean tensor

Variables Details

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stdnorm

Value:

norm(0, 1)