def correlationMatrix(data):
_, numCols = shape(data)
def matrixEntry(i, j):
return correlation(getCol(data, i), getCol(data, j))
return generateMatrix(numCols, numCols, matrixEntry)
def correlationMatrix(data):
_, numCols = shape(data)
def matrixEntry(i, j):
return correlation(getCol(data, i), getCol(data, j))
return generateMatrix(numCols, numCols, matrixEntry)
def scale(dataMatrix):
nRows, nCols = shape(dataMatrix)
means = [mean(getCol(dataMatrix, j))
for j in range(nCols)]
stdevs = [stdDeviation(getCol(dataMatrix, j))
for j in range(nCols)]
return means, stdevs
def rescale(dataMatrix):
means, stdevs = scale(dataMatrix)
def rescaled(i, j):
if(stdevs[j] > 0):
return (dataMatrix[i][j] - means[j]) / stdevs[j]
else:
return dataMatrix[i][j]
nRows, nCols = shape(dataMatrix)
return generateMatrix(nRows, nCols, rescaled)
def deMeanMatrix(A):
nr, nc = shape(A)
colMean, _ = scale(A)
return generateMatrix(nr, nc, lambda i, j: A[i][j] - colMean[j])
def deMeanMatrix(A):
nr, nc = shape(A)
colMean, _ = scale(A)
return generateMatrix(nr, nc, lambda i, j: A[i][j] - colMean[j])
