def correctnessVerification(levelMatFileName, inputMatFileName, outputFileName):
"""Show the variation trend of distance between original
output and output after dimention reduction, with the
change of k. And write this trend in to file.
"""
levelMatFile = open(levelMatFileName,'r')
inputMatFile = open(inputMatFileName,'r')
outputFile = open(outputFileName,'w')
levelMat = readLevelMat(levelMatFile)
inputMat = readInputMat(inputMatFile)
#origial output matrix
outputMat = np.dot(levelMat, inputMat.T)
U, Sigma, Vt = np.linalg.svd(levelMat)
#change the threshold
k_v = []
distance_v = []
for k in range(1,101,1):
print k
reducedDim = svd.getDim(Sigma, k/100.0)
if reducedDim:
U_r, Sigma_r, Vt_r = svd.dimReduce(U, Sigma, Vt, reducedDim)
levelMat_reduce = np.dot(U_r, np.dot(np.diag(Sigma_r),Vt_r) )
outputMat_reduce = np.dot(levelMat_reduce, inputMat.T)
else:
print 'There is an error!\n'
return
distanceVector = np.sqrt(np.sum(np.square(outputMat_reduce-outputMat), axis=0))
distance = np.sum(distanceVector)/distanceVector.shape[0]
outputFile.write('Distance vector: \n%s\n'%distanceVector)
outputFile.write('Distance: %.8f\n\n'%distance)
k_v.append(k)
distance_v.append(distance)
#visualize output data
plt.plot(k_v, distance_v, 'r', linewidth=2)
plt.axis([0,100,0,300])
plt.title('Distance between original and reduced output\nalong with change of k');
plt.xlabel('k/%');plt.ylabel('distance');plt.grid(True)
plt.show()
def outputHistogram(levelMatFileName, inputMatFileName, k=0.99):
"""Plot output historgram of sublevel, comming from one original level.
And compare differences between original and reduced mode.
"""
k_str = raw_input("Please enter value of k (0.0~1.0 or 'q'): ")
if k_str == "q":
pass
else:
k = float(k_str)
levelMatFile = open(levelMatFileName, "r")
inputMatFile = open(inputMatFileName, "r")
levelMat = readLevelMat(levelMatFile)
inputMat = readInputMat(inputMatFile)
U, Sigma, Vt = np.linalg.svd(levelMat)
# original
U_org, Sigma_org, Vt_org = svd.dimReduce(U, Sigma, Vt, len(Sigma))
subLevelMat1_org = np.dot(np.diag(Sigma_org), Vt_org)
subLevelMat2_org = U_org
subLevelOutputMat1_org = np.dot(subLevelMat1_org, inputMat.T)
subLevelOutputMat2_org = np.dot(subLevelMat2_org, subLevelOutputMat1_org)
# reduced
reducedDim = svd.getDim(Sigma, k)
if reducedDim:
U_r, Sigma_r, Vt_r = svd.dimReduce(U, Sigma, Vt, reducedDim)
else:
print "There is an error!\n"
return
subLevelMat1_r = np.dot(np.diag(Sigma_r), Vt_r)
subLevelMat2_r = U_r
subLevelOutputMat1_r = np.dot(subLevelMat1_r, inputMat.T)
subLevelOutputMat2_r = np.dot(subLevelMat2_r, subLevelOutputMat1_r)
# plot
fig1 = plt.figure("Output histogram of sublevel 1")
plt.subplot(2, 1, 1)
plt.hist(
subLevelOutputMat1_org.reshape((subLevelOutputMat1_org.shape[0] * subLevelOutputMat1_org.shape[1],)),
100,
(-150, 150),
)
plt.xlabel("output_value")
plt.ylabel("amount")
plt.title("Original dimention (%d)" % len(Sigma))
plt.xlim(-150, 150)
plt.subplot(2, 1, 2)
plt.hist(
subLevelOutputMat1_r.reshape((subLevelOutputMat1_r.shape[0] * subLevelOutputMat1_r.shape[1],)), 100, (-150, 150)
)
plt.xlabel("output_value")
plt.ylabel("amount")
plt.title("Reduced dimention (%d)" % reducedDim)
plt.xlim(-150, 150)
fig2 = plt.figure("Output histogram of sublevel 2")
plt.subplot(2, 1, 1)
plt.hist(
subLevelOutputMat2_org.reshape((subLevelOutputMat2_org.shape[0] * subLevelOutputMat2_org.shape[1],)),
100,
(-150, 150),
)
plt.xlabel("output_value")
plt.ylabel("amount")
plt.title("Original dimention (%d)" % len(Sigma))
plt.xlim(-150, 150)
plt.subplot(2, 1, 2)
plt.hist(
subLevelOutputMat2_r.reshape((subLevelOutputMat2_r.shape[0] * subLevelOutputMat2_r.shape[1],)), 100, (-150, 150)
)
plt.xlabel("output_value")
plt.ylabel("amount")
plt.title("Reduced dimention (%d)" % reducedDim)
plt.xlim(-150, 150)
plt.show()