In [1]:
# First we load the file
file_location = '../results_database/text_wall_street_columns.hdf5'
run_name = '/test'
f = h5py.File(file_location, 'r')
# Now we need to get the letters and align them
text_directory = '../data/wall_street_letters.npy'
letters_sequence = np.load(text_directory)
Nletters = len(letters_sequence)
symbols = set(letters_sequence)
# Nexa parameters
Nspatial_clusters = 3
Ntime_clusters = 3
Nembedding = 3
parameters_string = '/' + str(Nspatial_clusters)
parameters_string += '-' + str(Ntime_clusters)
parameters_string += '-' + str(Nembedding)
nexa = f[run_name + parameters_string]
cluster_to_index = nexa['cluster_to_index']
matrix = np.zeros((10, 3))
for cluster in cluster_to_index:
cluster_indexes = cluster_to_index[str(cluster)]
for index in cluster_indexes:
first_index = index // 3
second_index = index % 3
matrix[first_index, second_index] = cluster
import matplotlib.pyplot as plt
%matplotlib inline
plt.matshow(matrix)
Out[1]:
In [2]:
# First we load the file
file_location = '../results_database/text_wall_street_columns.hdf5'
run_name = '/independent'
f = h5py.File(file_location, 'r')
# Now we need to get the letters and align them
text_directory = '../data/wall_street_letters.npy'
letters_sequence = np.load(text_directory)
Nletters = len(letters_sequence)
symbols = set(letters_sequence)
# Nexa parameters
Nspatial_clusters = 3
Ntime_clusters = 3
Nembedding = 3
parameters_string = '/' + str(Nspatial_clusters)
parameters_string += '-' + str(Ntime_clusters)
parameters_string += '-' + str(Nembedding)
nexa = f[run_name + parameters_string]
cluster_to_index = nexa['cluster_to_index']
matrix = np.zeros((10, 3))
for cluster in cluster_to_index:
cluster_indexes = cluster_to_index[str(cluster)]
for index in cluster_indexes:
first_index = index // 3
second_index = index % 3
matrix[first_index, second_index] = cluster
import matplotlib.pyplot as plt
%matplotlib inline
plt.matshow(matrix)
Out[2]: