Package rdkit :: Package ML :: Package Data :: Module Stats
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Module Stats

source code

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, 318.30, 637],...

Imports: numpy


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**

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, 318.30, 637], [2, 1.886, 2.920\
, 4.303, 6.965, 9.925, 22.330, 31.6], [3, 1.638, 2.353, 3.182, 4.541, \
5.841, 10.210, 12.92], [4, 1.533, 2.132, 2.776, 3.747, 4.604, 7.173, 8\
.610], [5, 1.476, 2.015, 2.571, 3.365, 4.032, 5.893, 6.869], [6, 1.440\
, 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, 1.860, 2.306, 2.896, 3.355, 4.\
501, 5.041], [9, 1.383, 1.833, 2.262, 2.821, 3.250, 4.297, 4.781], [10\
, 1.372, 1.812, 2.228, 2.764, 3.169, 4.144, 4.587], [11, 1.363, 1.796,\
...