def main():
X, Y = generate_blocks(n_samples=1)
# X, Y = generate_checker()
# X, Y = generate_easy(n_samples=1)
# X, Y = generate_big_checker()
crf = BinaryGridCRF()
# clf = StructuredPerceptron(problem=crf, max_iter=100)
clf = StructuredSVM(problem=crf, max_iter=200, C=100, verbose=10, check_constraints=True)
# clf = SubgradientStructuredSVM(problem=crf, max_iter=2000, C=100)
clf.fit(X, Y)
Y_pred = clf.predict(X)
i = 0
loss = 0
for x, y, y_pred in zip(X, Y, Y_pred):
loss += np.sum(y != y_pred)
fig, plots = plt.subplots(2, 2)
plots[0, 0].set_title("x")
plots[0, 0].imshow(x[:, :, 0], interpolation="nearest")
plots[0, 1].set_title("y")
plots[0, 1].imshow(y, interpolation="nearest")
plots[1, 0].set_title("unaries")
w_unaries = np.zeros(4)
w_unaries[0] = 1.0
y_unaries = crf.inference(x, w_unaries)
plots[1, 0].imshow(y_unaries, interpolation="nearest")
plots[1, 1].set_title("full crf")
plots[1, 1].imshow(y_pred, interpolation="nearest")
for plot in plots.ravel():
plot.set_axis_off()
fig.savefig("data_%03d.png" % i)
plt.close(fig)
i += 1
print("loss: %f" % loss)
def test_binary_crf_exhaustive():
# tests graph cut inference against brute force
# on random data / weights
np.random.seed(0)
for i in xrange(50):
x = np.random.uniform(-1, 1, size=(3, 3))
x = np.dstack([-x, np.zeros_like(x)])
crf = BinaryGridCRF()
w = np.random.uniform(-1, 1, size=2)
# check map inference
y_hat = crf.inference(x, w)
y_ex = exhausive_inference_binary(crf, x, w)
print(y_hat)
print(y_ex)
print("++++++++++++++++++++++")
assert_array_equal(y_hat, y_ex)
def test_binary_crf_exhaustive():
# tests graph cut inference against brute force
# on random data / weights
np.random.seed(0)
for i in xrange(50):
x = np.random.uniform(-1, 1, size=(3, 3))
x = np.dstack([-x, np.zeros_like(x)])
crf = BinaryGridCRF()
w = np.random.uniform(-1, 1, size=2)
# check map inference
y_hat = crf.inference(x, w)
y_ex = exhausive_inference_binary(crf, x, w)
print(y_hat)
print(y_ex)
print("++++++++++++++++++++++")
assert_array_equal(y_hat, y_ex)
def test_binary_grid_unaries():
# test handling on unaries for binary grid CRFs
for ds in toy_datasets.binary:
X, Y = ds(n_samples=1)
x, y = X[0], Y[0]
crf = BinaryGridCRF()
w_unaries_only = np.zeros(2)
w_unaries_only[0] = 1.
# test that inference with unaries only is the
# same as argmax
inf_unaries = crf.inference(x, w_unaries_only)
pw_z = np.zeros((2, 2), dtype=np.int32)
un = np.ascontiguousarray(-1000 * x).astype(np.int32)
unaries = binary_grid(un, pw_z)
assert_array_equal(inf_unaries, unaries)
assert_array_equal(inf_unaries, np.argmax(x, axis=2))
try:
assert (np.mean(inf_unaries == y) > 0.5)
except:
print(ds)
def test_binary_grid_unaries():
# test handling on unaries for binary grid CRFs
for ds in toy_datasets.binary:
X, Y = ds(n_samples=1)
x, y = X[0], Y[0]
crf = BinaryGridCRF()
w_unaries_only = np.zeros(2)
w_unaries_only[0] = 1.
# test that inference with unaries only is the
# same as argmax
inf_unaries = crf.inference(x, w_unaries_only)
pw_z = np.zeros((2, 2), dtype=np.int32)
un = np.ascontiguousarray(
-1000 * x).astype(np.int32)
unaries = binary_grid(un, pw_z)
assert_array_equal(inf_unaries, unaries)
assert_array_equal(inf_unaries, np.argmax(x, axis=2))
try:
assert(np.mean(inf_unaries == y) > 0.5)
except:
print(ds)
def main():
X, Y = generate_blocks(n_samples=1)
#X, Y = generate_checker()
#X, Y = generate_easy(n_samples=1)
#X, Y = generate_big_checker()
crf = BinaryGridCRF()
#clf = StructuredPerceptron(problem=crf, max_iter=100)
clf = StructuredSVM(problem=crf,
max_iter=200,
C=100,
verbose=10,
check_constraints=True)
#clf = SubgradientStructuredSVM(problem=crf, max_iter=2000, C=100)
clf.fit(X, Y)
Y_pred = clf.predict(X)
i = 0
loss = 0
for x, y, y_pred in zip(X, Y, Y_pred):
loss += np.sum(y != y_pred)
fig, plots = plt.subplots(2, 2)
plots[0, 0].set_title("x")
plots[0, 0].imshow(x[:, :, 0], interpolation='nearest')
plots[0, 1].set_title("y")
plots[0, 1].imshow(y, interpolation='nearest')
plots[1, 0].set_title("unaries")
w_unaries = np.zeros(4)
w_unaries[0] = 1.
y_unaries = crf.inference(x, w_unaries)
plots[1, 0].imshow(y_unaries, interpolation='nearest')
plots[1, 1].set_title("full crf")
plots[1, 1].imshow(y_pred, interpolation='nearest')
for plot in plots.ravel():
plot.set_axis_off()
fig.savefig("data_%03d.png" % i)
plt.close(fig)
i += 1
print("loss: %f" % loss)