def test_with_data_in_mem(self): import pyemma.coordinates as api data = [ np.random.random((100, 50)), np.random.random((103, 50)), np.random.random((33, 50)) ] reader = source(data) assert isinstance(reader, DataInMemory) tpca = api.pca(dim=2) n_centers = 10 km = api.cluster_kmeans(k=n_centers) disc = api.discretizer(reader, tpca, km) disc.parametrize() dtrajs = disc.dtrajs for dtraj in dtrajs: n_states = np.max((np.unique(dtraj))) self.assertGreaterEqual( n_centers - 1, n_states, "dtraj has more states than cluster centers")
def test_with_data_in_mem(self): import pyemma.coordinates as api data = [np.random.random((100, 50)), np.random.random((103, 50)), np.random.random((33, 50))] reader = api.memory_reader(data) tpca = api.pca(dim=2) n_centers = 10 km = api.kmeans(k=n_centers) disc = api.discretizer(reader, tpca, km) disc.parametrize() dtrajs = disc.dtrajs for dtraj in dtrajs: n_states = np.max((np.unique(dtraj))) self.assertGreaterEqual(n_centers - 1, n_states, "dtraj has more states than cluster centers")
#number of PCCA clusters n_sets = 3 print 'feat dimension' print feat.dimension() dataset = [] nlist = [] if 1: n_clusters = 200 tica_obj = coor.tica(dim=2, lag=tica_lagtime, kinetic_map=True) input_data = coor.cluster_kmeans(k=n_clusters, max_iter=50) disc = coor.discretizer(inp, tica_obj, input_data, stride=1, chunksize=10) disc.parametrize() print tica_obj.cumvar #TICA output is Y Y = tica_obj.get_output() print np.shape(Y) #print 'Y[0]' #print Y[0] print 'number of trajetories = ', np.shape(Y)[0] # #mapped_data is the TICA clustered data mapped to the microstates (so integer valued) mapped_data = input_data.dtrajs #plot tica free energy histogram plot if 1:
print 'feat dimension' print feat.dimension() dataset = [] nlist = [] if 1: n_clusters = 200 tica_obj = coor.tica( dim=2, lag=tica_lagtime, kinetic_map=True) input_data = coor.cluster_kmeans( k=n_clusters, max_iter=50) disc = coor.discretizer(inp, tica_obj, input_data, stride=1, chunksize=10) disc.parametrize() print tica_obj.cumvar #TICA output is Y Y = tica_obj.get_output() print np.shape(Y) #print 'Y[0]' #print Y[0] print 'number of trajetories = ', np.shape(Y)[0] # #mapped_data is the TICA clustered data mapped to the microstates (so integer valued) mapped_data =input_data.dtrajs #plot tica free energy histogram plot if 1: