def atest_theano_kernel_density_estimation():
num_instances = 5
spn = SpnFactory.theano_kernel_density_estimation(num_instances, vars)
print('Kernel density estimation (indicators)')
print(spn)
spn = SpnFactory.theano_kernel_density_estimation(num_instances,
vars,
sparse=True)
print('Sparse kernel density estimation')
print(spn)
spn = SpnFactory.theano_kernel_density_estimation(num_instances, vars,
freqs)
print('Kernel density estimation (smoothing)')
print(spn)
def atest_theano_nltcs_kernel_spn():
print('Loading datasets')
train, valid, test = dataset.load_train_val_test_csvs('nltcs')
n_instances = train.shape[0]
n_test_instances = test.shape[0]
# estimating the frequencies for the features
print('Estimating features')
freqs, features = dataset.data_2_freqs(train)
print('Build kernel density estimation')
spn = SpnFactory.theano_kernel_density_estimation(
n_instances,
features,
batch_size=n_test_instances,
sparse=True)
print('Evaluating on test')
# evaluating one at a time since we are using a sparse representation
lls = []
for i in range(test.shape[0]):
print('instance', i)
lls.append(spn.eval(test[i, :]))
print('Mean lls')
# avg_ll = sum(lls) / float(len(lls))
avg_ll = numpy.mean(lls)
print(avg_ll)
def atest_theano_nltcs_kernel_spn():
print('Loading datasets')
train, valid, test = dataset.load_train_val_test_csvs('nltcs')
n_instances = train.shape[0]
n_test_instances = test.shape[0]
# estimating the frequencies for the features
print('Estimating features')
freqs, features = dataset.data_2_freqs(train)
print('Build kernel density estimation')
spn = SpnFactory.theano_kernel_density_estimation(
n_instances,
features,
batch_size=n_test_instances,
sparse=True)
print('Evaluating on test')
# evaluating one at a time since we are using a sparse representation
lls = []
for i in range(test.shape[0]):
print('instance', i)
lls.append(spn.eval(test[i, :]))
print('Mean lls')
# avg_ll = sum(lls) / float(len(lls))
avg_ll = numpy.mean(lls)
print(avg_ll)
def atest_theano_kernel_density_estimation():
num_instances = 5
spn = SpnFactory.theano_kernel_density_estimation(num_instances,
vars)
print('Kernel density estimation (indicators)')
print(spn)
spn = SpnFactory.theano_kernel_density_estimation(num_instances,
vars,
sparse=True)
print('Sparse kernel density estimation')
print(spn)
spn = SpnFactory.theano_kernel_density_estimation(num_instances,
vars,
freqs)
print('Kernel density estimation (smoothing)')
print(spn)
def test_theano_kernel_density_estimation_categorical():
num_instances = 5
spn = SpnFactory.theano_kernel_density_estimation(num_instances,
vars,
node_dict=freqs,
alpha=0.1)
print('Sparse kernel density estimation' +
'with smoothed categorical input layer')
print(spn)
print(spn.stats())
def test_theano_kernel_density_estimation_categorical():
num_instances = 5
spn = SpnFactory.theano_kernel_density_estimation(num_instances,
vars,
node_dict=freqs,
alpha=0.1)
print('Sparse kernel density estimation' +
'with smoothed categorical input layer')
print(spn)
print(spn.stats())
