def get_factors_codes_dataset():
''' Create factors-codes dataset
'''
# create factors dataset
dist, dist_kwargs = DISTRIBUTION
factors = get_artificial_factors_dataset(nb_examples=NB_EXAMPLES,
nb_factors=NB_FACTORS,
distribution=dist,
dist_kwargs=dist_kwargs)
# compute codes from continuous factors
codes = np.concatenate((factors, factors, factors, factors), axis=1)
return factors, codes
def get_factors_codes_dataset(nb_seen_factors):
''' Create factors-codes dataset
:param nb_seen_factors: number of measured factors
'''
# create factors dataset
dist, dist_kwargs = DISTRIBUTION
factors = get_artificial_factors_dataset(nb_examples=NB_EXAMPLES,
nb_factors=NB_FACTORS,
distribution=dist,
dist_kwargs=dist_kwargs)
# compute codes from continuous factors
codes = factors
factors = factors[:, :nb_seen_factors]
return factors, codes
def get_factors_codes_dataset(noise_level):
''' Create factors-codes dataset
:param noise_level: noise level to compute codes from factors
'''
# create factors dataset
dist, dist_kwargs = DISTRIBUTION
factors = get_artificial_factors_dataset(nb_examples=NB_EXAMPLES,
nb_factors=NB_FACTORS,
distribution=dist,
dist_kwargs=dist_kwargs)
# compute codes from continuous factors
noise = np.random.uniform(size=factors.shape)
codes = (1 - noise_level) * factors + noise_level * noise
return factors, codes
def get_factors_codes_dataset(non_linearity_strength):
''' Create factors-codes dataset
:param non_linearity_strength: strength of non-linearity relation between factors and codes
'''
# create factors dataset
dist, dist_kwargs = DISTRIBUTION
factors = get_artificial_factors_dataset(nb_examples=NB_EXAMPLES,
nb_factors=NB_FACTORS,
distribution=dist,
dist_kwargs=dist_kwargs)
# compute codes from continuous factors
B, C = 1000, 0.25
omega = 2 * np.arctan(B**(non_linearity_strength - C) / 2)
codes = B**(C - non_linearity_strength) * np.tan(omega *
(factors - 0.5)) + 0.5
return factors, codes
def get_factors_codes_dataset(noise_level):
''' Create factors-codes dataset
:param noise_level: noise level to compute codes from factors
'''
# create factors dataset
dist, dist_kwargs = DISTRIBUTION
factors = get_artificial_factors_dataset(nb_examples=NB_EXAMPLES,
nb_factors=NB_FACTORS,
distribution=dist,
dist_kwargs=dist_kwargs)
# create projection matrix
projection = (1 - noise_level) * np.eye(NB_FACTORS) + noise_level * np.eye(
NB_FACTORS, k=1)
projection[-1, 0] = noise_level
# compute codes from continuous factors
codes = np.dot(factors, projection)
return factors, codes