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7f235b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | import os,sys
import numpy
import random
import math
import torch
def splitScalar_NFold(X,Y,N_fold):
nt = X.shape[0]
assert Y.shape[0] == nt
aX = []
aY = []
for j in range(N_fold):
aX.append(X[j])
aY.append(numpy.array([Y[j]]))
for i in range(N_fold,nt):
j = random.randint(0,N_fold-1)
aX[j] = numpy.vstack( (aX[j],X[i,:]) )
aY[j] = numpy.append(aY[j],Y[i])
return aX,aY
def split_train_validate(aX,aT,ind_fold):
N = len(aX)
m = aX[0].shape[1]
Xt = numpy.empty((0,m))
Tt = numpy.empty((0,))
Xv = numpy.empty((0,m))
Tv = numpy.empty((0,))
for j in range(N):
if j == ind_fold:
Xv = numpy.vstack((Xv,aX[j]))
Tv = numpy.append(Tv,aT[j])
else:
Xt = numpy.vstack((Xt,aX[j]))
Tt = numpy.append(Tt,aT[j])
Xt = Xt.astype(numpy.float32)
Tt = Tt.astype(numpy.float32)
Xv = Xv.astype(numpy.float32)
Tv = Tv.astype(numpy.float32)
return Xt,Tt,Xv,Tv
def LeastSquare_SVD(X,Y,alpha0):
nt = X.shape[0]
one = numpy.ones((nt,1))
X1 = numpy.hstack((X,one))
Ux, Dx, Vx = numpy.linalg.svd(X1, full_matrices=False)
nt0 = Dx.shape[0]
for i in range(nt0):
x = Dx[i]
Dx[i] = x/(x*x+alpha0)
DxInv = numpy.diag(Dx)
W0 = numpy.dot(numpy.dot(Vx.T, DxInv), Ux.T)
W1 = Y
print("W0: "+str(W0.shape))
print("w1: "+str(W1.shape))
return W0,W1
def LeastSquare_SVD_Eval(X,W0,W1):
nt = X.shape[0]
one = numpy.ones((nt,1))
X1 = numpy.hstack((X,one))
XW0 = numpy.dot(X1,W0)
XW0W1 = numpy.dot(XW0,W1)
return XW0W1
def loadXT():
X = numpy.load("I2_0.npy")
X = numpy.vstack((X,numpy.load("I2_1.npy")))
X = numpy.vstack((X,numpy.load("I2_2.npy")))
X = numpy.vstack((X,numpy.load("I2_3.npy")))
X = numpy.vstack((X,numpy.load("I2_4.npy")))
X = numpy.vstack((X,numpy.load("I2_5.npy")))
X = numpy.vstack((X,numpy.load("I2_6.npy")))
X = numpy.vstack((X,numpy.load("I2_7.npy")))
X = numpy.vstack((X,numpy.load("I2_8.npy")))
T = numpy.load("Cd_0.npy")
T = numpy.append(T, numpy.load("Cd_1.npy"))
T = numpy.append(T, numpy.load("Cd_2.npy"))
T = numpy.append(T, numpy.load("Cd_3.npy"))
T = numpy.append(T, numpy.load("Cd_4.npy"))
T = numpy.append(T, numpy.load("Cd_5.npy"))
T = numpy.append(T, numpy.load("Cd_6.npy"))
T = numpy.append(T, numpy.load("Cd_7.npy"))
T = numpy.append(T, numpy.load("Cd_8.npy"))
return X,T
def main():
X,T = loadXT()
assert( X.shape[0] == T.shape[0] )
N_fold = 9
N_sample = 4
ES = []
for ind_sample in range(N_sample):
aX,aT = splitScalar_NFold(X,T,N_fold)
for ind_fold in range(N_fold):
print("n-fold split",ind_sample,ind_fold,aX[ind_fold].shape," ",aT[ind_fold].shape)
E = []
for ind_fold in range(N_fold):
Xt,Tt,Xv,Tv = split_train_validate(aX,aT,ind_fold)
print(ind_sample,ind_fold)
print("train size:",Xt.shape,Tt.shape)
print("validate size:",Xv.shape,Tv.shape)
W0,W1 = LeastSquare_SVD(Xt,Tt,0.8)
Yv = LeastSquare_SVD_Eval(Xv,W0,W1)
Ev = Yv-Tv
E.extend(Ev)
E = numpy.asarray(E)
assert E.shape[0] == X.shape[0]
stdE = numpy.linalg.norm(E)
stdE = math.sqrt(stdE*stdE/E.shape[0])
print("standard deviation",stdE)
ES.append(stdE)
ES = numpy.asarray(ES)
numpy.savetxt("StandardDeviations"+".txt",ES)
if __name__ == "__main__":
main()
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