def __init__(self, neutral, smiling, batch_size=300, epochs=1,
alpha=1):
d = neutral.shape[1]
self.W = 0
self.b = 0
lasso = Lasso(alpha=alpha, warm_start=True)
lasso.coef_ = np.eye(d, d)
for _ in range(epochs):
print "epoch", _
js = np.random.random_integers(0, neutral.shape[0] - 1, size=batch_size)
X = lemur_util.distort(neutral[js])
Y = lemur_util.distort(smiling[js])
model = lasso.fit(X, Y)
self.W += model.coef_
self.b += model.intercept_
print self.W.shape, self.b.shape
self.W /= epochs
self.b /= epochs
self.b.reshape((1, d))
neutral.append(scipy.ndimage.imread(filename, flatten=True).flatten() / 255)
neutral = np.array(neutral)
# Load (and repeat) smiling faces
with LemurTimer("loading smiling faces"):
smiling = []
for filename in sorted(glob.glob(SMILING_FACES_GLOB)):
smiling.append(scipy.ndimage.imread(filename, flatten=True).flatten() / 255)
smiling = np.array(smiling)
# Approximate PCA by the lemur method :P
with LemurTimer("approximating PCA"):
pca = LemurPCA(PCA_BATCH_SIZE, PCA_EPOCHS)
# Perform Kernel Ridge Regression using pca as the kernel
neutral_training = lemur_util.distort(neutral[:N_TRAINING].repeat(REPEAT, axis=0))
smiling_training = smiling[:N_TRAINING].repeat(REPEAT, axis=0)
with LemurTimer("performing KRR"):
krr = LemurKRR(pca, neutral_training, smiling_training)
# Predict the smiling faces from the neutral ones
with LemurTimer("predicting smiling faces"):
prediction = krr(neutral[-N_TEST:]).T
# Display the neutral, smiling and predicted faces side to side
# one row per test image
f = plt.figure()
for i in range(N_TEST):
k = N_TRAINING + i
f.add_subplot(N_TEST, 3, 3*i + 1)
