def test_kcenters_8():
X = np.random.RandomState(1).randn(100, 2)
X32 = X.astype(np.float32)
X64 = X.astype(np.float64)
m1 = KCenters(n_clusters=10, random_state=0).fit([X32])
m2 = KCenters(n_clusters=10, random_state=0).fit([X64])
eq(m1.cluster_centers_, m2.cluster_centers_)
eq(m1.distances_[0], m2.distances_[0])
eq(m1.labels_[0], m2.labels_[0])
assert np.all(np.logical_not(np.isnan(m1.distances_[0])))
eq(m1.predict([X32])[0], m2.predict([X64])[0])
eq(m1.predict([X32])[0], m1.labels_[0])
eq(float(m1.inertia_), libdistance.assign_nearest(X32, m1.cluster_centers_, "euclidean")[1])
def test_kcenters_3():
# test for predict using euclidean distance
m = KCenters(n_clusters=10)
data = np.random.randn(100, 2)
labels1 = m.fit_predict([data])
labels2 = m.predict([data])
eq(labels1[0], labels2[0])
all_pairs = scipy.spatial.distance.cdist(data, m.cluster_centers_)
eq(labels2[0], np.argmin(all_pairs, axis=1))
def test_kcenters_4():
# test for predict() using non-euclidean distance. because of the
# way the code is structructured, this takes a different path
model = KCenters(n_clusters=10, metric='cityblock')
data = np.random.randn(100, 2)
labels1 = model.fit_predict([data])
labels2 = model.predict([data])
eq(labels1[0], labels2[0])
all_pairs = scipy.spatial.distance.cdist(data, model.cluster_centers_, metric='cityblock')
eq(labels2[0], np.argmin(all_pairs, axis=1))
draw_tica_projection_cross_validation(
sub_resultdir,
'Fold_%d_tica_lagtime_%d_train_data_proj_tIC13.png' %
(fold, tica_correlation_time), train_data_projection,
test_data_projection, 1, 3)
for n_tics in n_tics_range:
for n_Micro in n_Micro_range:
print("parameters: fold-", fold, ',tica_lagtime-',
tica_correlation_time, ',n_tics-', n_tics,
',n_Micro-', n_Micro)
kcenters = KCenters(n_clusters=n_Micro,
metric='euclidean',
random_state=0)
kcenters.fit(train_data_projection)
train_data_sequence = kcenters.predict(
train_data_projection)
test_data_sequence = kcenters.predict(test_data_projection)
msm = MarkovStateModel(
n_timescales=3,
lag_time=100,
reversible_type='transpose',
verbose=False,
sliding_window=True,
ergodic_cutoff='on') #the parameters may change
msm.fit(train_data_sequence)
train_score = msm.score(train_data_sequence)
test_score = msm.score(test_data_sequence)
f1 = open(
sub_resultdir +
'/Fold_%d_tica_lagtime_%d_ntics_%d_nMicro_%d_gmrq.summary'
% (fold, tica_correlation_time, n_tics, n_Micro), 'w')
