def compute(self, X, y):
XC = asColumnMatrix(X)
y = np.asarray(y)
d = XC.shape[0]
c = len(np.unique(y))
if self._num_components<=0 or self._num_components>(c-1):
self._num_components = c-1
meanTotal = XC.mean(axis=1).reshape(-1,1)
Sw = np.zeros((d,d), dtype=np.float32)
Sb = np.zeros((d,d), dtype=np.float32)
for i in range(0,c):
Xi = XC[:, np.where(y==i)[0]]
meanClass = np.mean(Xi, axis=1).reshape(-1,1)
Sw = Sw + np.dot((Xi-meanClass),(Xi-meanClass).T)
Sb = Sb + Xi.shape[1] * np.dot((meanClass-meanTotal),(meanClass-meanTotal).T)
self._eigenvalues, self._eigenvectors = np.linalg.eig(np.linalg.inv(Sw)*Sb)
idx = np.argsort(-self._eigenvalues.real)
self._eigenvalues, self._eigenvectors = self._eigenvalues[idx], self._eigenvectors[:,idx]
self._eigenvalues = np.array(self._eigenvalues[0:self._num_components].real, dtype=np.float32, copy=True)
self._eigenvectors = np.array(self._eigenvectors[0:,0:self._num_components].real, dtype=np.float32, copy=True)
features = []
for x in X:
xp = self.project(x.reshape(-1,1))
features.append(xp)
return features
def compute(self, X, y):
XC = asColumnMatrix(X)
y = np.asarray(y)
pca = PCA(num_components=(len(y) - len(np.unique(y))))
lda = LDA(num_components=self._num_components)
model = ChainOperator(pca, lda)
model.compute(X, y)
self._eigenvalues = lda.eigenvalues
self._num_components = lda.num_components
self._eigenvectors = np.dot(pca.eigenvectors, lda.eigenvectors)
features = []
for x in X:
xp = self.project(x.reshape(-1,1))
features.append(xp)
return features
def compute(self, X, y):
XC = asColumnMatrix(X)
y = np.asarray(y)
if self._num_components<=0 or self._num_components>(XC.shape[1]-1):
self._num_components = XC.shape[1]-1
self._mean = XC.mean(axis=1).reshape(-1,1)
XC = XC-self._mean
self._eigenvectors, self._eigenvalues, variances = np.linalg.svd(XC, full_matrices=False)
idx = np.argsort(-self._eigenvalues)
self._eigenvalues, self._eigenvectors = self._eigenvalues[idx], self._eigenvectors[:,idx]
self._eigenvectors = self._eigenvectors[0:,0:self._num_components].copy()
self._eigenvalues = self._eigenvalues[0:self._num_components].copy()
self._eigenvalues = np.power(self._num_components,2) / XC.shape[1]
features = []
for x in X:
xp = self.project(x.reshape(-1,1))
features.append(xp)
return features