Datasets:
jblitzar
/
github-python-corpus

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testSize = len(testSet)
trainSize = origSize - testSize
bp = GGS(trainData.T, breakpoints, lamb, [], verbose)[0]
for z in bp:
i = z
(mse, currBreak) = (0, 1)
temp = trainData[0:i[1]]
empMean = np.mean(temp, axis=0)
empCov = np.cov(temp.T,bias = True) + float(lamb)*np.identity(n)/temp.shape[0]
invCov = np.linalg.inv(empCov)
#Calculate test error
for j in range(testSize):
#Find which break it's in
adj = testSet[j] - j
cb = max(sum(1 for k in i if k < adj),1)
if (currBreak != cb):
currBreak = cb
temp = trainData[i[currBreak-1]:i[currBreak]]
empMean = np.mean(temp, axis=0)
empCov = np.cov(temp.T,bias = True) + float(lamb)*np.identity(n)/temp.shape[0]
invCov = np.linalg.inv(empCov)
#Compute likelihood
ldet = 0.5*np.linalg.slogdet(invCov)[1]
ll = ldet - 0.5*(data[testSet[j]] - empMean).dot(invCov).dot((data[testSet[j]] - empMean)) - n*math.log(2*math.pi)/2
mse = mse+ll
mseList.append((len(i)-2, mse/testSize))
#Calculate training error
tErr = 0
currBreak = 1
temp = trainData[0:i[1]]
empMean = np.mean(temp, axis=0)
empCov = np.cov(temp.T,bias = True) + float(lamb)*np.identity(n)/temp.shape[0]
invCov = np.linalg.inv(empCov)
for j in range(1,trainSize):
if(j in i):
currBreak = currBreak + 1
temp = trainData[i[currBreak-1]:i[currBreak]]
empMean = np.mean(temp, axis=0)
empCov = np.cov(temp.T,bias = True) + float(lamb)*np.identity(n)/temp.shape[0]
invCov = np.linalg.inv(empCov)
#Compute likelihood
ldet = 0.5*np.linalg.slogdet(invCov)[1]
ll = ldet - 0.5*(trainData[j] - empMean).dot(invCov).dot((trainData[j] - empMean)) - n*math.log(2*math.pi)/2
tErr = tErr+ll
trainList.append((len(i)-2, tErr/trainSize))
return mseList, trainList