Package ML :: Package Data :: Module Stats

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Module Stats

various statistical operations on data

Functions

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

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], ...

Complex0 = 'F'

Complex16 = 'F'

Complex32 = 'F'

Complex64 = 'D'

Complex8 = 'F'

Float0 = 'f'

Float16 = 'f'

Float32 = 'f'

Float64 = 'd'

Float8 = 'f'

Int0 = '1'

Int16 = 's'

Int32 = 'i'

Int8 = '1'

absolute = <ufunc 'absolute'>

add = <ufunc 'add'>

arccos = <ufunc 'arccos'>

arccosh = <ufunc 'arccosh'>

arcsin = <ufunc 'arcsin'>

arcsinh = <ufunc 'arcsinh'>

arctan = <ufunc 'arctan'>

arctan2 = <ufunc 'arctan2'>

arctanh = <ufunc 'arctanh'>

bitwise_and = <ufunc 'bitwise_and'>

bitwise_or = <ufunc 'bitwise_or'>

bitwise_xor = <ufunc 'bitwise_xor'>

ceil = <ufunc 'ceil'>

conjugate = <ufunc 'conjugate'>

cos = <ufunc 'cos'>

cosh = <ufunc 'cosh'>

divide = <ufunc 'divide'>

divide_safe = <ufunc 'divide_safe'>

e = 2.71828182846

equal = <ufunc 'equal'>

exp = <ufunc 'exp'>

fabs = <ufunc 'fabs'>

floor = <ufunc 'floor'>

floor_divide = <ufunc 'floor_divide'>

fmod = <ufunc 'fmod'>

greater = <ufunc 'greater'>

greater_equal = <ufunc 'greater_equal'>

hypot = <ufunc 'hypot'>

invert = <ufunc 'invert'>

left_shift = <ufunc 'left_shift'>

less = <ufunc 'less'>

less_equal = <ufunc 'less_equal'>

log = <ufunc 'log'>

log10 = <ufunc 'log10'>

logical_and = <ufunc 'logical_and'>

logical_not = <ufunc 'logical_not'>

logical_or = <ufunc 'logical_or'>

logical_xor = <ufunc 'logical_xor'>

maximum = <ufunc 'maximum'>

minimum = <ufunc 'minimum'>

multiply = <ufunc 'multiply'>

negative = <ufunc 'negative'>

not_equal = <ufunc 'not_equal'>

pi = 3.14159265359

power = <ufunc 'power'>

remainder = <ufunc 'remainder'>

right_shift = <ufunc 'right_shift'>

sin = <ufunc 'sin'>

sinh = <ufunc 'sinh'>

sqrt = <ufunc 'sqrt'>

subtract = <ufunc 'subtract'>

tan = <ufunc 'tan'>

tanh = <ufunc 'tanh'>

true_divide = <ufunc 'true_divide'>

Function Details

StandardizeMatrix(mat)



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

 **Arguments**

   - mat: a Numeric array

 **Notes**

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

FormCovarianceMatrix(mat)

form and return the covariance matrix

FormCorrelationMatrix(mat)

form and return the covariance matrix

PrincipalComponents(mat, reverseOrder=1)

do a principal components analysis

TransformPoints(tFormMat, pts)

transforms a set of points using tFormMat

**Arguments**

  - tFormMat: a Numeric array

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

**Returns**

  a list of Numeric arrays

Variables Details

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],
...