Package ML :: Package Data :: Module Stats

Module Stats

various statistical operations on data

Functions

[hide private]

StandardizeMatrix(mat)
This is the standard *subtract off the average and divide by the deviation* standardization function.

source code

FormCovarianceMatrix(mat)
form and return the covariance matrix...

source code

FormCorrelationMatrix(mat)
form and return the covariance matrix...

source code

PrincipalComponents(mat, reverseOrder=1)
do a principal components analysis...

source code

TransformPoints(tFormMat, pts)
transforms a set of points using tFormMat...

source code

MeanAndDev(vect, sampleSD=1)
returns the mean and standard deviation of a vector

source code

R2(orig, residSum)
returns the R2 value for a set of predictions

source code

GetConfidenceInterval(sd, n, level=95)

source code

Variables

[hide private]

tConfs = {80: 1, 90: 2, 95: 3, 98: 4, 99: 5, 99.8: 6, 99.9: 7}

tTable = [[1, 3.078, 6.314, 12.71, 31.82, 63.66, 318.3, 637], ...

Function Details

[hide private]

StandardizeMatrix(mat)

source code



This is the standard *subtract off the average and divide by the deviation*
standardization function.

 **Arguments**

   - mat: a numpy array

 **Notes**

   - in addition to being returned, _mat_ is modified in place, so **beware**

FormCovarianceMatrix(mat)

source code

form and return the covariance matrix

FormCorrelationMatrix(mat)

source code

form and return the covariance matrix

PrincipalComponents(mat, reverseOrder=1)

source code

do a principal components analysis

TransformPoints(tFormMat, pts)

source code

transforms a set of points using tFormMat

**Arguments**

  - tFormMat: a numpy array

  - pts: a list of numpy arrays (or a 2D array)

**Returns**

  a list of numpy arrays

Variables Details

[hide private]

tTable

Value:

[[1, 3.078, 6.314, 12.71, 31.82, 63.66, 318.3, 637],
 [2, 1.886, 2.92, 4.303, 6.965, 9.925, 22.33, 31.6],
 [3, 1.638, 2.353, 3.182, 4.541, 5.841, 10.21, 12.92],
 [4, 1.533, 2.132, 2.776, 3.747, 4.604, 7.173, 8.61],
 [5, 1.476, 2.015, 2.571, 3.365, 4.032, 5.893, 6.869],
 [6, 1.44, 1.943, 2.447, 3.143, 3.707, 5.208, 5.959],
 [7, 1.415, 1.895, 2.365, 2.998, 3.499, 4.785, 5.408],
 [8, 1.397, 1.86, 2.306, 2.896, 3.355, 4.501, 5.041],
...