def compute(self, X, y):
# turn into numpy representation
Xc = asColumnMatrix(X)
y = np.asarray(y)
# gather some statistics about the dataset
n = len(y)
c = len(np.unique(y))
# define features to be extracted
pca = PCA(num_components = (n-c))
lda = LDA(num_components = self._num_components)
# fisherfaces are a chained feature of PCA followed by LDA
model = ChainOperator(pca,lda)
# computing the chained model then calculates both decompositions
model.compute(X,y)
# store eigenvalues and number of components used
self._eigenvalues = lda.eigenvalues
self._num_components = lda.num_components
# compute the new eigenspace as pca.eigenvectors*lda.eigenvectors
self._eigenvectors = np.dot(pca.eigenvectors,lda.eigenvectors)
# finally compute the features (these are the Fisherfaces)
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
