def main():
# create mrf problem:
# two nodes, binary problem
# potts potential
pairwise = [[0, 1, 1], [1, 0, 1], [1, 1, 0]]
import toy_datasets as toy
X, Y = toy.generate_blocks_multinomial(n_samples=1, noise=.5)
x, y = X[0], Y[0]
inds = np.arange(x.shape[0] * x.shape[1]).reshape(x.shape[:2])
inds = inds.astype(np.int64)
horz = np.c_[inds[:, :-1].ravel(), inds[:, 1:].ravel()]
vert = np.c_[inds[:-1, :].ravel(), inds[1:, :].ravel()]
edges = np.vstack([horz, vert])
x = x.reshape(-1, x.shape[-1])
unary_assignment = solve_lp(-x, pairwise, edges)
plt.matshow(np.argmax(unary_assignment, axis=1).reshape(y.shape))
plt.show()
tracer()
import toy_datasets as toy
import matplotlib.pyplot as pyplot
from itertools import product
# Define constants
N = 1
size = 12
noise = 1.25
# Data generation
X, Y = toy.generate_blocks_multinomial(n_samples=N, noise=noise, n_rows=size, n_cols=size)
# Save files
pyplot.matshow(Y[0])
pyplot.xticks([])
pyplot.yticks([])
pyplot.savefig('figures/toy_label.svg', format='svg', bbox_inches=0)
pyplot.matshow(X[0,:,:,0])
pyplot.xticks([])
pyplot.yticks([])
pyplot.savefig('fugres/toy_feature_0.svg', format='svg', bbox_inches=0)
pyplot.matshow(X[0,:,:,1])
pyplot.xticks([])
pyplot.yticks([])
pyplot.savefig('figures/toy_feature_1.svg', format='svg', bbox_inches=0)
import toy_datasets as toy
import matplotlib.pyplot as pyplot
from itertools import product
# Define constants
N = 1
size = 12
noise = 1.25
# Data generation
X, Y = toy.generate_blocks_multinomial(n_samples=N,
noise=noise,
n_rows=size,
n_cols=size)
# Save files
pyplot.matshow(Y[0])
pyplot.xticks([])
pyplot.yticks([])
pyplot.savefig('figures/toy_label.svg', format='svg', bbox_inches=0)
pyplot.matshow(X[0, :, :, 0])
pyplot.xticks([])
pyplot.yticks([])
pyplot.savefig('fugres/toy_feature_0.svg', format='svg', bbox_inches=0)
pyplot.matshow(X[0, :, :, 1])
pyplot.xticks([])
pyplot.yticks([])
pyplot.savefig('figures/toy_feature_1.svg', format='svg', bbox_inches=0)
