import numpy as np
def rescaleNormalization(dataArray):
min = dataArray.min()
denom = dataArray.max() – min
newValues = []
for x in dataArray:
newX = (x – min) / denom
newValues.append(newX)
return newValues
def rescaleMatrix(dataMatrix):
colCount = len(dataMatrix[0])
rowCount = len(dataMatrix)
newMatrix = np.zeros(dataMatrix.shape)
for i in range(0, colCount):
min = dataMatrix[:,i].min()
denom = dataMatrix[:,i].max() – min
for k in range(0, rowCount):
newX = (dataMatrix[k,i] – min) / denom
newMatrix[k,i] = newX
return newMatrix
def meanNormalization(dataArray):
mean = np.mean(dataArray)
denom = dataArray.max() – dataArray.min()
newValues = []
for x in dataArray:
newX = (x – mean) / denom
newValues.append(newX)
return newValues