def validate(self, reader):
validateFiles = reader.testdata
triggers = reader.classtestTriggers
onsets = reader.classtestOnsets
finOnsets = reader.classtestFinOnsets
[validateData,
validateLabels] = utilities.dataLoadFromEDF(self, validateFiles,
triggers, onsets,
finOnsets, self.params)
numChannels = validateData.shape[1]
numSamples = validateData.shape[0]
sumFvecLen = 0
for ch in range(numChannels):
if not (self.params.fDiaps is None):
self.params.lowFreq = self.params.fDiaps[ch][0]
self.params.highFreq = self.params.fDiaps[ch][1]
tmpVec = utilities.get1DFeatures(
np.squeeze(self.trainData[0, ch, :]), self.params)
sumFvecLen = sumFvecLen + len(tmpVec)
fVecs = np.zeros((numSamples, sumFvecLen))
for k in range(numSamples):
#print('Validate Sample ', k, ' of ', numSamples)
curFvec = np.zeros((0))
for i in range(numChannels):
if not (self.params.fDiaps is None):
self.params.lowFreq = self.params.fDiaps[i][0]
self.params.highFreq = self.params.fDiaps[i][1]
chVec = utilities.get1DFeatures(
np.squeeze(validateData[k, i, :]), self.params)
curFvec = np.concatenate((curFvec, chVec), axis=0)
fVecs[k, :] = curFvec
#Norm!
for i in range(fVecs.shape[0]):
fVecs[i, :] = (fVecs[i, :] -
self.trainResult.mean) / self.trainResult.std
fTransformed = fVecs
if self.params.usePCA:
fTransformed = self.trainResult.pcaOp.transform(fVecs)
if (not self.params.finalClassifier is None):
fTransformed = utilities.applyClassifier(
self.params.finalClassifier, self.trainResult.finalOp,
fTransformed)
result = np.zeros((fTransformed.shape[0]))
for i, fvec in enumerate(fTransformed):
if not (self.params.distanceFun is None):
result[i] = self.params.distanceFun(
self.trainResult.trainTransformedVecs,
self.trainResult.trainLabels, fvec, self.params)
else:
result[i] = fTransformed[i]
classRates, confMat = utilities.calcStats(validateLabels, result)
#print('Class Rates:\n', classRates)
#print('Confusion Matrix: \n', confMat)
return classRates, confMat
def validate(self, reader):
validateFiles = reader.testdata
triggers = reader.classtestTriggers
onsets = reader.classtestOnsets
finOnsets = reader.classtestFinOnsets
[validateData,
validateLabels] = utilities.dataLoadFromEDF(self, validateFiles,
triggers, onsets,
finOnsets, self.params)
numChannels = validateData.shape[1]
numSamples = validateData.shape[0]
tmpVec = utilities.get1DFeatures(np.squeeze(validateData[0, 0, :]),
self.params)
fvecLen = len(tmpVec)
fVecs = np.zeros((numSamples, numChannels, fvecLen))
for k in range(numSamples):
#print("Fvec: " + str(k) + " of " + str(numSamples))
for i in range(numChannels):
fVecs[k, i, :] = utilities.get1DFeatures(
np.squeeze(validateData[k, i, :]), self.params)
#Norm!
fTransformed = np.zeros(
(fVecs.shape[0], fVecs.shape[1] * fVecs.shape[2]))
if (self.params.usePCA):
fTransformed = np.zeros(
(fVecs.shape[0], fVecs.shape[1] * self.params.numPC))
for i in range(numChannels):
chanVecs = np.squeeze(fVecs[:, i, :])
for k in range(chanVecs.shape[0]):
chanVecs[k, :] = (chanVecs[k, :] - self.trainResult.mean[i]
) / self.trainResult.std[i]
if (self.params.usePCA):
fTransformed[:, i * self.params.numPC:(i + 1) *
self.params.numPC] = self.trainResult.pcaOp[
i].transform(chanVecs)
else:
fTransformed[:, i * fVecs.shape[2]:(i + 1) *
fVecs.shape[2]] = chanVecs
if (not self.params.finalClassifier is None):
fTransformed = utilities.applyClassifier(
self.params.finalClassifier, self.trainResult.finalOp,
fTransformed)
result = np.zeros((fTransformed.shape[0]))
for i, fvec in enumerate(fTransformed):
if not (self.params.distanceFun is None):
result[i] = self.params.distanceFun(
self.trainResult.trainTransformedVecs,
self.trainResult.trainLabels, fvec, self.params)
else:
result[i] = fTransformed[i]
classRates, confMat = utilities.calcStats(validateLabels, result)
#print('Class Rates:\n', classRates)
#print('Confusion Matrix: \n', confMat)
return classRates, confMat
def autoSelectComps(ClassifierParams, reader, compThresh, minCompsNum = None):
trainFiles = reader.traindata
triggers = reader.trainTriggers
onsets = reader.trainOnsets
finOnsets = reader.trainFinOnsets
trainData, labels, trainonsets, trainfinOnsets = utilities.continuousFromEDF(trainFiles, triggers, onsets,
finOnsets, ClassifierParams)
useIca = ClassifierParams.useICA
if (minCompsNum is None):
minCompsNum = 2
# if ClassifierParams.usePCA:
# if not (ClassifierParams.numPC is None):
# if (minCompsNum<ClassifierParams.numPC):
# minCompsNum = ClassifierParams.numPC + 1
icaMatFileName = ClassifierParams.icaFile
if useIca:
if (icaMatFileName is None) or not (os.path.isfile(icaMatFileName)):
calcAndSaveICA(ClassifierParams, reader, icaMatFileName)
icaMat = scipy.io.loadmat(icaMatFileName)['T']
dataToProcess_ = np.matmul(icaMat, trainData)
print('Got ICA!')
else:
dataToProcess_ = trainData
[trainData, labels] = utilities.dataLoadFromContinuous(ClassifierParams, dataToProcess_, labels, trainonsets,
trainfinOnsets)
numChannels = trainData.shape[1]
numSamples = trainData.shape[0]
tmpVec = utilities.get1DFeatures(np.squeeze(trainData[0, 0, :]), ClassifierParams)
fvecLen = len(tmpVec)
fVecs_ = np.zeros((numSamples, numChannels, fvecLen))
for k in range(numSamples):
# print("Fvec: " + str(k) + " of " + str(numSamples))
for i in range(numChannels):
fVecs_[k, i, :] = utilities.get1DFeatures(np.squeeze(trainData[k, i, :]),
ClassifierParams)
# choosing components
N = trainData.shape[2]
NFFT = int(math.pow(2, int(math.ceil(math.log(N, 2)))))
T = 1.0 / ClassifierParams.Fs
freqs = fftfreq(NFFT, T)
freqs = freqs[1:NFFT // 2]
idx = np.where((freqs > ClassifierParams.lowFreq) * (freqs < ClassifierParams.highFreq))
freqs = freqs[idx]
print('Calcuating individual components and frequencies...')
comps, diaps = autoSpectralChoose(fVecs_, labels, freqs, minFreq=1.5, maxFreq=18, freqWin=3, t=compThresh, minCompsNum=minCompsNum)
return comps, diaps
def processChunk(self, rawTest):
testChunk = utilities.preprocessDataChunk(rawTest, self)
numChannels = testChunk.shape[0]
curFvec = np.array([])
for ch in range(numChannels):
if not (self.params.fDiaps is None):
self.params.lowFreq = self.params.fDiaps[ch][0]
self.params.highFreq = self.params.fDiaps[ch][1]
chVec = utilities.get1DFeatures(
np.squeeze(self.trainData[0, ch, :]), self.params)
curFvec = np.concatenate((curFvec, chVec))
# Norm!
curFvec = (curFvec - self.trainResult.mean) / self.trainResult.std
fTransformed = np.expand_dims(curFvec, 0)
if self.params.usePCA:
fTransformed = self.trainResult.pcaOp.transform(fTransformed)
if (not self.params.finalClassifier is None):
fTransformed = utilities.applyClassifier(
self.params.finalClassifier, self.trainResult.finalOp,
fTransformed)
if not (self.params.distanceFun is None):
result = self.params.distanceFun(
self.trainResult.trainTransformedVecs,
self.trainResult.trainLabels, fTransformed, self.params)
else:
result = fTransformed
return result
def train(self, reader, doBalanceLabels):
trainFiles = reader.traindata
triggers = reader.classtrainTriggers
onsets = reader.classtrainOnsets
finOnsets = reader.classtrainFinOnsets
[self.trainData,
self.labels] = utilities.dataLoadFromEDF(self, trainFiles, triggers,
onsets, finOnsets,
self.params)
if (doBalanceLabels):
[self.trainData,
self.labels] = utilities.balance_labels(self.trainData,
self.labels)
numChannels = self.trainData.shape[1]
numSamples = self.trainData.shape[0]
fvecLen = len(self.params.channelSelect)
fVecs = np.zeros((numSamples, fvecLen))
for k in range(numSamples):
#print("Fvec: " + str(k) + " of " + str(numSamples))
for i in range(fvecLen):
sample = np.squeeze(self.trainData[k, i, :])
if not (self.params.fDiaps is None):
self.params.lowFreq = self.params.fDiaps[i][0]
self.params.highFreq = self.params.fDiaps[i][1]
specVal = np.mean(utilities.get1DFeatures(
sample, self.params)) #!!!!!!!
fVecs[k, i] = specVal
#Shuffle!
inds = np.random.permutation(fVecs.shape[0])
fVecs = fVecs[inds, :]
labels = self.labels[inds]
self.trainResult.mean = np.mean(fVecs, 0)
self.trainResult.std = np.std(fVecs, 0)
#Norm!
for i in range(fVecs.shape[0]):
fVecs[i, :] = (fVecs[i, :] -
self.trainResult.mean) / self.trainResult.std
fTransformed = fVecs
# PCA!
if self.params.usePCA:
pcaTransform = PCA(self.params.numPC)
pcaTransform.fit(fVecs)
self.trainResult.pcaOp = pcaTransform
fTransformed = pcaTransform.transform(fVecs)
#LDA!
if not (self.params.finalClassifier is None):
[Op, fTransformed
] = utilities.trainClassifier(self.params.finalClassifier,
fTransformed, labels)
self.trainResult.finalOp = Op
if not (self.params.distanceFun is None):
self.trainResult.trainTransformedVecs = fTransformed
self.trainResult.trainLabels = labels
def validate(self, reader):
validateFiles = reader.testdata
triggers = reader.classtestTriggers
onsets = reader.classtestOnsets
finOnsets = reader.classtestFinOnsets
[validateData,
validateLabels] = utilities.dataLoadFromEDF(self, validateFiles,
triggers, onsets,
finOnsets, self.params)
numChannels = validateData.shape[1]
numSamples = validateData.shape[0]
fvecLen = len(self.params.channelSelect)
fVecs = np.zeros((numSamples, fvecLen))
for k in range(numSamples):
#print("Fvec: " + str(k) + " of " + str(numSamples))
for i in range(fvecLen):
sample = np.squeeze(validateData[k, i, :])
if not (self.params.fDiaps is None):
self.params.lowFreq = self.params.fDiaps[i][0]
self.params.highFreq = self.params.fDiaps[i][1]
specVal = np.mean(utilities.get1DFeatures(sample, self.params))
fVecs[k, i] = specVal
#Norm!
for i in range(fVecs.shape[0]):
fVecs[i, :] = (fVecs[i, :] -
self.trainResult.mean) / self.trainResult.std
fTransformed = fVecs
if self.params.usePCA:
fTransformed = self.trainResult.pcaOp.transform(fVecs)
if (not self.params.finalClassifier is None):
fTransformed = utilities.applyClassifier(
self.params.finalClassifier, self.trainResult.finalOp,
fTransformed)
result = np.zeros((fTransformed.shape[0]))
for i, fvec in enumerate(fTransformed):
if not (self.params.distanceFun is None):
result[i] = self.params.distanceFun(
self.trainResult.trainTransformedVecs,
self.trainResult.trainLabels, fvec, self.params)
else:
result[i] = fTransformed[i]
classRates, confMat = utilities.calcStats(validateLabels, result)
#print('Class Rates:\n', classRates)
#print('Confusion Matrix: \n', confMat)
return classRates, confMat
def train(self, reader, doBalanceLabels):
trainFiles = reader.traindata
triggers = reader.classtrainTriggers
onsets = reader.classtrainOnsets
finOnsets = reader.classtrainFinOnsets
[self.trainData,
self.labels] = utilities.dataLoadFromEDF(self, trainFiles, triggers,
onsets, finOnsets,
self.params)
if (doBalanceLabels):
[self.trainData,
self.labels] = utilities.balance_labels(self.trainData,
self.labels)
numChannels = self.trainData.shape[1]
numSamples = self.trainData.shape[0]
sumFvecLen = 0
for ch in range(numChannels):
if not (self.params.fDiaps is None):
self.params.lowFreq = self.params.fDiaps[ch][0]
self.params.highFreq = self.params.fDiaps[ch][1]
tmpVec = utilities.get1DFeatures(
np.squeeze(self.trainData[0, ch, :]), self.params)
sumFvecLen = sumFvecLen + len(tmpVec)
fVecs = np.zeros((numSamples, sumFvecLen))
for k in range(numSamples):
#print('Train Sample ', k, ' of ', numSamples)
curFvec = np.zeros((0))
for i in range(numChannels):
if not (self.params.fDiaps is None):
self.params.lowFreq = self.params.fDiaps[i][0]
self.params.highFreq = self.params.fDiaps[i][1]
chVec = utilities.get1DFeatures(
np.squeeze(self.trainData[k, i, :]), self.params)
curFvec = np.concatenate((curFvec, chVec), axis=0)
fVecs[k, :] = curFvec
#Shuffle!
inds = np.random.permutation(fVecs.shape[0])
fVecs = fVecs[inds, :]
labels = self.labels[inds]
self.trainResult.mean = np.mean(fVecs, 0)
self.trainResult.std = np.std(fVecs, 0)
#Norm!
for i in range(fVecs.shape[0]):
fVecs[i, :] = (fVecs[i, :] -
self.trainResult.mean) / self.trainResult.std
# PCA!
fTransformed = fVecs
if self.params.usePCA:
pcaTransform = PCA(self.params.numPC)
pcaTransform.fit(fVecs)
self.trainResult.pcaOp = pcaTransform
fTransformed = pcaTransform.transform(fVecs)
#LDA!
if not (self.params.finalClassifier is None):
[Op, fTransformed
] = utilities.trainClassifier(self.params.finalClassifier,
fTransformed, labels)
self.trainResult.finalOp = Op
self.trainResult.trainLabels = labels
if not (self.params.distanceFun is None):
self.trainResult.trainTransformedVecs = fTransformed
def train(self, reader, doBalanceLabels):
trainFiles = reader.traindata
triggers = reader.classtrainTriggers
onsets = reader.classtrainOnsets
finOnsets = reader.classtrainFinOnsets
[self.trainData,
self.labels] = utilities.dataLoadFromEDF(self, trainFiles, triggers,
onsets, finOnsets,
self.params)
if (doBalanceLabels):
[self.trainData,
self.labels] = utilities.balance_labels(self.trainData,
self.labels)
numChannels = self.trainData.shape[1]
numSamples = self.trainData.shape[0]
tmpVec = utilities.get1DFeatures(np.squeeze(self.trainData[0, 0, :]),
self.params)
fvecLen = len(tmpVec)
fVecs = np.zeros((numSamples, numChannels, fvecLen))
for k in range(numSamples):
#print("Fvec: " + str(k) + " of " + str(numSamples))
for i in range(numChannels):
fVecs[k, i, :] = utilities.get1DFeatures(
np.squeeze(self.trainData[k, i, :]), self.params)
#Shuffle!
inds = np.random.permutation(fVecs.shape[0])
fVecs = fVecs[inds, :, :]
labels = self.labels[inds]
#Norm!
for i in range(fVecs.shape[1]):
chanVecs = np.squeeze(fVecs[:, i, :])
self.trainResult.mean.append(np.mean(chanVecs, 0))
self.trainResult.std.append(np.std(chanVecs, 0))
for k in range(chanVecs.shape[0]):
chanVecs[k, :] = (chanVecs[k, :] - self.trainResult.mean[i]
) / self.trainResult.std[i]
fVecs[:, i, :] = chanVecs
fTransformed = np.zeros(
(fVecs.shape[0], fVecs.shape[1] * fVecs.shape[2]))
if (self.params.usePCA):
fTransformed = np.zeros(
(fVecs.shape[0], fVecs.shape[1] * self.params.numPC))
# PCA or reshaping:
for i in range(numChannels):
chanVecs = np.squeeze(fVecs[:, i, :])
if (self.params.usePCA):
curPcaTransform = PCA(self.params.numPC)
curPcaTransform.fit(chanVecs)
self.trainResult.pcaOp.append(curPcaTransform)
fTransformed[:, i * self.params.numPC:(i + 1) *
self.params.numPC] = curPcaTransform.transform(
chanVecs)
else:
fTransformed[:, i * fVecs.shape[2]:(i + 1) *
fVecs.shape[2]] = chanVecs
#LDA!
if not (self.params.finalClassifier is None):
[Op, fTransformed
] = utilities.trainClassifier(self.params.finalClassifier,
fTransformed, labels)
self.trainResult.finalOp = Op
if not (self.params.distanceFun is None):
self.trainResult.trainTransformedVecs = fTransformed
self.trainResult.trainLabels = labels
def train(self, reader, doBalanceLabels):
trainFiles = reader.traindata
triggers = reader.classtrainTriggers
onsets = reader.classtrainOnsets
finOnsets = reader.classtrainFinOnsets
[self.trainData, self.labels] = utilities.dataLoadFromEDF(self, trainFiles, triggers, onsets, finOnsets,
self.params)
if (doBalanceLabels):
[self.trainData, self.labels] = utilities.balance_labels(self.trainData, self.labels)
numChannels = self.trainData.shape[1]
numSamples = self.trainData.shape[0]
if self.mode == 'IndepChan':
tmpVec = utilities.get1DFeatures(np.squeeze(self.trainData[0, 0, :]), self.params)
sumFvecLen = len(tmpVec)
print(sumFvecLen, self.trainData.shape)
print(self.trainData[0, 0, :])
if self.mode == 'CustomFreqs':
sumFvecLen = len(self.params.channelSelect)
print(sumFvecLen, '\n', self.params.channelSelect)
if self.mode == 'Classic':
sumFvecLen = 0
for i in range(numChannels):
if not (self.params.fDiaps is None):
self.params.lowFreq = self.params.fDiaps[i][0]
self.params.highFreq = self.params.fDiaps[i][1]
tmpVec = utilities.get1DFeatures(np.squeeze(self.trainData[0, i, :]), self.params)
sumFvecLen = sumFvecLen + len(tmpVec)
#print(sumFvecLen, '\n', self.trainData[0, i, :], '\n', tmpVec)
fVecs = np.zeros((numSamples, numChannels, sumFvecLen))
#Fastovets
#indepchan [nchan = 75 x nvecs = 335 x vecLen = 49] 139
#classic [nchan = 75 x nvecs = 335 x vecLen = 49] 2293
#custom [nchan = 75 x nvecs = 335 x vecLen = 29]
#tmpVec.shape = 16
for k in range(numSamples): #cust classic = 1
curFvec = np.zeros((0))
# print("Fvec: " + str(k) + " of " + str(numSamples))
for i in range(numChannels):
if not (self.params.fDiaps is None):
self.params.lowFreq = self.params.fDiaps[i][0]
self.params.highFreq = self.params.fDiaps[i][1]
tmpVec = utilities.get1DFeatures(np.squeeze(self.trainData[k, i, :]), self.params)
if self.mode == 'IndepChan':
fVecs[k, i, :] = tmpVec #"""КАК НАСЧЕТ ТОГО, ЧТОБЫ СОБЛЮДАТЬ РАЗМЕРНОСТЬ FVECS?""" [k, i, :]
if self.mode == 'CustomFreqs':
specVal = np.mean(tmpVec) # !!!!!!! was not meaned while sumfveclen was counted!
fVecs[k, i, 0] = specVal #[k, i]
if self.mode == 'Classic':
curFvec = np.concatenate((curFvec, tmpVec), axis=0)
if self.mode == 'Classic':
fVecs[k, i, :] = curFvec #[k, :]
# Shuffle!
inds = np.random.permutation(fVecs.shape[0])
fVecs = fVecs[inds, :, :]
labels = self.labels[inds]
#print(self.trainResult.mean, self.trainResult.std)
# Norm!
if self.mode == 'Classic' or self.mode == 'CustomFreqs':
self.trainResult.mean = np.mean(fVecs, 0)
self.trainResult.std = np.std(fVecs, 0)
for i in range(fVecs.shape[0]):
fVecs[i, :, :] = (fVecs[i, :, :] - self.trainResult.mean) / self.trainResult.std
fTransformed = fVecs
#PCA
if self.params.usePCA: #cycle! as in INDEPCHAN
for i in range(fVecs.shape[1]):
tmp = np.zeros((fVecs.shape[0], fVecs.shape(2)))
tmp = fVecs(:, i, :)
pcaTransform = PCA(self.params.numPC)
pcaTransform.fit(tmp)
self.trainResult.pcaOp = pcaTransform
fTransformed = pcaTransform.transform(tmp)
def validate(self, reader):
validateFiles = reader.testdata
triggers = reader.classtestTriggers
onsets = reader.classtestOnsets
finOnsets = reader.classtestFinOnsets
[validateData, validateLabels] = utilities.dataLoadFromEDF(self, validateFiles, triggers, onsets, finOnsets,
self.params)
numChannels = validateData.shape[1]
numSamples = validateData.shape[0]
if self.mode == 'IndepChan':
tmpVec = utilities.get1DFeatures(np.squeeze(validateData[0, 0, :]), self.params)
sumFvecLen = len(tmpVec)
if self.mode == 'CustomFreqs':
sumFvecLen = len(self.params.channelSelect)
if self.mode == 'Classic':
sumFvecLen = 0
for i in range(numChannels):
if not (self.params.fDiaps is None):
self.params.lowFreq = self.params.fDiaps[i][0]
self.params.highFreq = self.params.fDiaps[i][1]
tmpVec = utilities.get1DFeatures(np.squeeze(validateData[0, i, :]), self.params)
sumFvecLen = sumFvecLen + len(tmpVec)
fVecs = np.zeros((numSamples, numChannels, sumFvecLen))
for k in range(numSamples): #cust classic = 1
curFvec = np.zeros((0))
# print("Fvec: " + str(k) + " of " + str(numSamples))
for i in range(numChannels):
if not (self.params.fDiaps is None):
self.params.lowFreq = self.params.fDiaps[i][0]
self.params.highFreq = self.params.fDiaps[i][1]
tmpVec = utilities.get1DFeatures(np.squeeze(self.trainData[k, i, :]), self.params)
if self.mode == 'IndepChan':
fVecs[k, i, :] = tmpVec #"""КАК НАСЧЕТ ТОГО, ЧТОБЫ СОБЛЮДАТЬ РАЗМЕРНОСТЬ FVECS?""" [k, i, :]
if self.mode == 'CustomFreqs':
specVal = np.mean(tmpVec) # !!!!!!! was not meaned while sumfveclen was counted!
fVecs[k, i, 0] = specVal #[k, i]
if self.mode == 'Classic':
curFvec = np.concatenate((curFvec, tmpVec), axis=0)
if self.mode == 'Classic':
fVecs[k, i, :] = curFvec #[k, :]
# Norm!
if self.mode == 'Classic' or self.mode == 'CustomFreqs':
self.trainResult.mean = np.mean(fVecs, 0)
self.trainResult.std = np.std(fVecs, 0)
for i in range(fVecs.shape[0]):
fVecs[i, :, :] = (fVecs[i, :, :] - self.trainResult.mean) / self.trainResult.std
fTransformed = fVecs
# PCA
if self.params.usePCA:
fTransformed = self.trainResult.pcaOp.transform(fVecs)
#Norm!
if self.mode == 'IndepChan':
fTransformed = np.zeros((fVecs.shape[0], fVecs.shape[1] * fVecs.shape[2]))
# PCA
if (self.params.usePCA):
fTransformed = np.zeros((fVecs.shape[0], fVecs.shape[1] * self.params.numPC))
for i in range(numChannels):
chanVecs = np.squeeze(fVecs[:, i, :])
for k in range(chanVecs.shape[0]):
chanVecs[k, :] = (chanVecs[k, :] - self.trainResult.mean[i]) / self.trainResult.std[i]
if (self.params.usePCA):
fTransformed[:, i * self.params.numPC:(i + 1) * self.params.numPC] = self.trainResult.pcaOp[
i].transform(chanVecs)
else:
fTransformed[:, i * fVecs.shape[2]:(i + 1) * fVecs.shape[2]] = chanVecs
if (not self.params.finalClassifier is None):
fTransformed = utilities.applyClassifier(self.params.finalClassifier, self.trainResult.finalOp,
fTransformed)
result = np.zeros((fTransformed.shape[0]))
for i, fvec in enumerate(fTransformed):
if not (self.params.distanceFun is None):
result[i] = self.params.distanceFun(self.trainResult.trainTransformedVecs, self.trainResult.trainLabels,
fvec, self.params)
else:
result[i] = fTransformed[i]
classRates, confMat = utilities.calcStats(validateLabels, result)
# print('Class Rates:\n', classRates)
# print('Confusion Matrix: \n', confMat)
return classRates, confMat