def with_aureliens_potentials_svm(test=False):
data = load_data('train', independent=True)
data = add_kraehenbuehl_features(data)
features = [x[0] for x in data.X]
y = np.hstack(data.Y)
if test:
data_ = load_data('val', independent=True)
data_ = add_kraehenbuehl_features(data_)
features.extend([x[0] for x in data.X])
y = np.hstack([y, np.hstack(data_.Y)])
new_features_flat = np.vstack(features)
from sklearn.svm import LinearSVC
print("training svm")
svm = LinearSVC(C=.001, dual=False, class_weight='auto')
svm.fit(new_features_flat[y != 21], y[y != 21])
print(svm.score(new_features_flat[y != 21], y[y != 21]))
print("evaluating")
eval_on_pixels(data, [svm.predict(x) for x in features])
if test:
print("test data")
data_val = load_data('test', independent=True)
else:
data_val = load_data('val', independent=True)
data_val = add_kraehenbuehl_features(data_val)
features_val = [x[0] for x in data_val.X]
eval_on_pixels(data_val, [svm.predict(x) for x in features_val])
def with_aureliens_potentials_svm(test=False):
data = load_data('train', independent=True)
data = add_kraehenbuehl_features(data)
features = [x[0] for x in data.X]
y = np.hstack(data.Y)
if test:
data_ = load_data('val', independent=True)
data_ = add_kraehenbuehl_features(data_)
features.extend([x[0] for x in data.X])
y = np.hstack([y, np.hstack(data_.Y)])
new_features_flat = np.vstack(features)
from sklearn.svm import LinearSVC
print("training svm")
svm = LinearSVC(C=.001, dual=False, class_weight='auto')
svm.fit(new_features_flat[y != 21], y[y != 21])
print(svm.score(new_features_flat[y != 21], y[y != 21]))
print("evaluating")
eval_on_pixels(data, [svm.predict(x) for x in features])
if test:
print("test data")
data_val = load_data('test', independent=True)
else:
data_val = load_data('val', independent=True)
data_val = add_kraehenbuehl_features(data_val)
features_val = [x[0] for x in data_val.X]
eval_on_pixels(data_val, [svm.predict(x) for x in features_val])
def on_slic_superpixels():
data = load_data('train', independent=True)
probs = get_kraehenbuehl_pot_sp(data)
results = eval_on_pixels(data, [np.argmax(prob, axis=-1) for prob in
probs])
plt.matshow(results['confusion'])
plt.show()
def on_slic_superpixels():
data = load_data('train', independent=True)
probs = get_kraehenbuehl_pot_sp(data)
results = eval_on_pixels(data,
[np.argmax(prob, axis=-1) for prob in probs])
plt.matshow(results['confusion'])
plt.show()
