import kmeans
from sklearn import cross_validation, ensemble, metrics, svm
import random_search as rs
num_components = 128 # rs.pick_one(64, 128)()
# patch_height = int(np.round(rs.uniform(0.2, 1.0)() * (num_components/2 + 1)))
patch_height = int(np.round(rs.uniform(0.5, 1.0)() * (num_components/2 + 1)))
settings = {
# prepro + specgram extraction
'normalise_volume': True,
'specgram_num_components': num_components,
'specgram_redundancy': rs.pick_one(2, 4)(), # 1 is no overlap between successive windows, 2 is half overlap, 4 is three quarter overlap
'log_scale': rs.log_uniform(10**0, 10**1)(), # rs.log_uniform(10**0, 10**2)(), # rs.log_uniform(10**-1, 10**6)(),
# local specgram normalisation
'lnorm': True, # rs.pick_one(True, False)(),
'lnorm_sigma_mean': rs.log_uniform(2, 8)(),
'lnorm_sigma_std': rs.log_uniform(3, 7)(), # rs.log_uniform(2, 8)(),
# patch extraction
'patch_height': patch_height, # number of spectrogram components
'patch_width': rs.uniform_int(6, 16)(), # number of timesteps
'num_patches_for_learning': 100000,
# whitening
'retain': 1 - rs.log_uniform(10**-1, 10**-5)(),
'pca_bias': 0.0001,
from sklearn import cross_validation, ensemble, metrics, svm
import random_search as rs
num_components = 128 # rs.pick_one(64, 128)()
# patch_height = int(np.round(rs.uniform(0.2, 1.0)() * (num_components/2 + 1)))
patch_height = int(np.round(rs.uniform(0.5, 1.0)() * (num_components / 2 + 1)))
settings = {
# prepro + specgram extraction
'normalise_volume': True,
'specgram_num_components': num_components,
'specgram_redundancy': rs.pick_one(2, 4)(
), # 1 is no overlap between successive windows, 2 is half overlap, 4 is three quarter overlap
'log_scale': rs.log_uniform(10**0, 10**1)
(), # rs.log_uniform(10**0, 10**2)(), # rs.log_uniform(10**-1, 10**6)(),
# local specgram normalisation
'lnorm': True, # rs.pick_one(True, False)(),
'lnorm_sigma_mean': rs.log_uniform(2, 8)(),
'lnorm_sigma_std': rs.log_uniform(3, 7)(), # rs.log_uniform(2, 8)(),
# patch extraction
'patch_height': patch_height, # number of spectrogram components
'patch_width': rs.uniform_int(6, 16)(), # number of timesteps
'num_patches_for_learning': 100000,
# whitening
'retain': 1 - rs.log_uniform(10**-1, 10**-5)(),
'pca_bias': 0.0001,
import kmeans
from sklearn import cross_validation, ensemble, metrics, svm
import random_search as rs
num_components = rs.pick_one(64, 128)()
patch_height = int(np.round(rs.uniform(0.2, 1.0)() * (num_components/2 + 1)))
settings = {
# prepro + specgram extraction
'normalise_volume': True,
'specgram_num_components': num_components,
'specgram_redundancy': rs.pick_one(2, 4)(), # 1 is no overlap between successive windows, 2 is half overlap, 4 is three quarter overlap
'log_scale': rs.log_uniform(10**-1, 10**6)(),
# patch extraction
'patch_height': patch_height, # number of spectrogram components
'patch_width': rs.uniform_int(6, 16)(), # number of timesteps
'num_patches_for_learning': 100000,
# whitening
'retain': 1 - rs.log_uniform(10**-1, 10**-5)(),
'pca_bias': 0.0001,
# kmeans
'num_means': 1000,
# extraction
'threshold': rs.pick_one(None, rs.uniform(0.0, 3.0)())(),
import kmeans
from sklearn import cross_validation, ensemble, metrics, svm
import random_search as rs
num_components = rs.pick_one(64, 128)()
patch_height = int(np.round(rs.uniform(0.2, 1.0)() * (num_components / 2 + 1)))
settings = {
# prepro + specgram extraction
'normalise_volume': True,
'specgram_num_components': num_components,
'specgram_redundancy': rs.pick_one(2, 4)(
), # 1 is no overlap between successive windows, 2 is half overlap, 4 is three quarter overlap
'log_scale': rs.log_uniform(10**-1, 10**6)(),
# patch extraction
'patch_height': patch_height, # number of spectrogram components
'patch_width': rs.uniform_int(6, 16)(), # number of timesteps
'num_patches_for_learning': 100000,
# whitening
'retain': 1 - rs.log_uniform(10**-1, 10**-5)(),
'pca_bias': 0.0001,
# kmeans
'num_means': 1000,
# extraction
'threshold': rs.pick_one(None,
