def test_mvtsne_error():
# Data and is_distane do not have the same length.
one = np.arange(25, dtype=float).reshape((5, 5))
two = np.arange(25, 50, dtype=float).reshape((5, 5))
data = [one, two]
is_distance = [False, False, False]
mvtsne_est = mvtsne.MvtSNE(k=2)
assert_raises(ValueError, mvtsne_est.fit, data, is_distance)
# Sample data matrices do not have the same number of rows
one = np.arange(25, dtype=float).reshape((5, 5))
two = np.arange(25, 49, dtype=float).reshape((4, 6))
data = [one, two]
is_distance = [False, False]
mvtsne_est = mvtsne.MvtSNE(k=2)
assert_raises(ValueError, mvtsne_est.fit, data, is_distance)
# k value cannot be negative
one = np.arange(25, dtype=float).reshape((5, 5))
two = np.arange(25, 50, dtype=float).reshape((5, 5))
data = [one, two]
is_distance = [False, False]
mvtsne_est = mvtsne.MvtSNE(k=-2)
assert_raises(ValueError, mvtsne_est.fit, data, is_distance)
def test_mvtsne():
one = np.arange(25, dtype=float).reshape((5, 5))
two = np.arange(25, 50, dtype=float).reshape((5, 5))
data = [one, two]
is_distance = [False, False]
mvtsne_est = mvtsne.MvtSNE(k=2)
mvtsne_est.fit(data, is_distance)
def getEmbedding(self, rd_state=3):
"""
use multiview tsne to embed the components to 2d plane
type rd_state: int: random state
"""
self.rd_state = rd_state
is_distance = [False] * len(self.data)
mvtsne_est = mvtsne.MvtSNE(k=2,
perplexity=10,
random_state=self.rd_state,
epoch=3000)
mvtsne_est.fit(self.data, is_distance)
self.X_embedded = np.asarray(mvtsne_est.embedding_)
def savePatternEmbedding(self):
self.loadFactors()
self.data = [
np.array([
self.factors[i][j].tolist() for i in range(len(self.factors))
]) for j in range(self.column_cnt)
]
is_distance = [False] * len(self.data)
mvmds_est = mvmds.MVMDS(k=2)
self.factor_embeddings = {}
mvmds_est.fit(self.data, is_distance)
self.factor_embeddings['mds'] = mvmds_est.components_.tolist()
self.rd_state = 5
is_distance = [False] * len(self.data)
mvtsne_est = mvtsne.MvtSNE(k=2,
perplexity=10,
random_state=self.rd_state,
epoch=3000)
mvtsne_est.fit(self.data, is_distance)
self.factor_embeddings['tsne'] = np.asarray(
mvtsne_est.embedding_).tolist()
mvsc_est = mvsc.MVSC(k=2)
mvsc_est.fit(self.data, is_distance)
self.factor_embeddings['sc'] = np.asarray(mvsc_est.evectors_).tolist()
# cpc_est = cpcmv.MVCPC(k=2)
# self.factor_embeddings['sc'] = cpc_est.fit(self.data)[1].tolist()
with open(
'/home/xidao/project/thesis/iFac/src/src/data/' + self.domain +
'/factors_' + str(self.column_cnt) + '_' + str(self.cur_base) +
'_sample_pattern_embedding.json', 'w') as fp:
json.dump(self.factor_embeddings, fp)