The crosses are placed randomly in the image and noise is added.
The center state is not encoded in the input, so that the task can not be
solved without pairwise interactions.
"""
import numpy as np
import matplotlib.pyplot as plt
from pystruct.models import GridCRF
import pystruct.learners as ssvm
from pystruct.datasets import generate_crosses_explicit
from pystruct.utils import expand_sym
X, Y = generate_crosses_explicit(n_samples=50, noise=10)
crf = GridCRF(neighborhood=4)
clf = ssvm.OneSlackSSVM(model=crf, C=100, n_jobs=-1, inference_cache=100,
tol=.1)
clf.fit(X, Y)
Y_pred = np.array(clf.predict(X))
print("overall accuracy (training set): %f" % clf.score(X, Y))
# plot one example
x, y, y_pred = X[0], Y[0], Y_pred[0]
y_pred = y_pred.reshape(x.shape[:2])
fig, plots = plt.subplots(1, 4, figsize=(12, 4))
plots[0].matshow(y)
plots[0].set_title("ground truth")
plots[1].matshow(np.argmax(x, axis=-1))
as well as the different algorithms.
We use exact inference here, so the plots are easier to interpret.
As this is a small toy example, it is hard to generalize
the results indicated in the plot to more realistic settigs.
"""
import numpy as np
import matplotlib.pyplot as plt
from pystruct.models import GridCRF
from pystruct.learners import (NSlackSSVM, OneSlackSSVM, SubgradientSSVM,
FrankWolfeSSVM)
from pystruct.datasets import generate_crosses_explicit
X, Y = generate_crosses_explicit(n_samples=50, noise=10, size=6, n_crosses=1)
n_labels = len(np.unique(Y))
crf = GridCRF(n_states=n_labels, inference_method=("ad3", {'branch_and_bound': True}))
n_slack_svm = NSlackSSVM(crf, check_constraints=False,
max_iter=50, batch_size=1, tol=0.001)
one_slack_svm = OneSlackSSVM(crf, check_constraints=False,
max_iter=100, tol=0.001, inference_cache=50)
subgradient_svm = SubgradientSSVM(crf, learning_rate=0.001, max_iter=20,
decay_exponent=0, momentum=0)
bcfw_svm = FrankWolfeSSVM(crf, max_iter=50, check_dual_every=4)
#n-slack cutting plane ssvm
n_slack_svm.fit(X, Y)
# 1-slack cutting plane ssvm
as well as the different algorithms.
We use exact inference here, so the plots are easier to interpret.
As this is a small toy example, it is hard to generalize
the results indicated in the plot to more realistic settigs.
"""
import numpy as np
import matplotlib.pyplot as plt
from pystruct.models import GridCRF
from pystruct.learners import (NSlackSSVM, OneSlackSSVM, SubgradientSSVM,
FrankWolfeSSVM)
from pystruct.datasets import generate_crosses_explicit
X, Y = generate_crosses_explicit(n_samples=50, noise=10, size=6, n_crosses=1)
n_labels = len(np.unique(Y))
crf = GridCRF(n_states=n_labels,
inference_method=("ad3", {
'branch_and_bound': True
}))
n_slack_svm = NSlackSSVM(crf,
check_constraints=False,
max_iter=50,
batch_size=1,
tol=0.001)
one_slack_svm = OneSlackSSVM(crf,
check_constraints=False,
max_iter=100,
tol=0.001,
The inputs contain a cross pattern with a separate state for the center.
The crosses are placed randomly in the image and noise is added.
The center state is not encoded in the input, so that the task can not be
solved without pairwise interactions.
"""
import numpy as np
import matplotlib.pyplot as plt
from pystruct.models import GridCRF
import pystruct.learners as ssvm
from pystruct.datasets import generate_crosses_explicit
from pystruct.utils import expand_sym
X, Y = generate_crosses_explicit(n_samples=50, noise=10)
crf = GridCRF(neighborhood=4)
clf = ssvm.OneSlackSSVM(model=crf, C=100, inference_cache=100,
tol=.1)
clf.fit(X, Y)
print Y.shape
Y_pred = np.array(clf.predict(X))
print("overall accuracy (training set): %f" % clf.score(X, Y))
# plot one example
x, y, y_pred = X[0], Y[0], Y_pred[0]
y_pred = y_pred.reshape(x.shape[:2])
fig, plots = plt.subplots(1, 4, figsize=(12, 4))
plots[0].matshow(y)
plots[0].set_title("ground truth")
