def complexity_measure(mat, dist):
x, y = load_data(mat)
prop = calculate_dist(y, x, dist)
f = open("../Output/Results/complexity.txt", "a+")
f.write("Mat:" + mat + "," + str(prop) + "\n")
def cross_fold_ldcrf(mat, dist, number_folds, n_jobs, our_states, normal_states):
x, y = load_data(mat)
results = dict()
results["final"] = []
for i in range(number_folds):
test_index = list(range(i, len(x), number_folds))
validation_index = list(set(range(len(x))) - set(test_index))
# Loads and split data
x_v, y_v = np.array(x)[validation_index], np.array(y)[validation_index]
x_t, y_t = np.array(x)[test_index], np.array(y)[test_index]
# Does the test
results["final"].append(test(our_states, normal_states, x_v, y_v, x_t, y_t, dist, n_jobs, i))
def cross_fold_ldcrf(mat, dist, number_folds, n_jobs, our_states,
normal_states):
x, y = load_data(mat)
results = dict()
results["final"] = []
for i in range(number_folds):
test_index = list(range(i, len(x), number_folds))
validation_index = list(set(range(len(x))) - set(test_index))
# Loads and split data
x_v, y_v = np.array(x)[validation_index], np.array(y)[validation_index]
x_t, y_t = np.array(x)[test_index], np.array(y)[test_index]
# Does the test
results["final"].append(
test(our_states, normal_states, x_v, y_v, x_t, y_t, dist, n_jobs,
i))
def cross_fold_ldcrf(mat, dist, labels, number_folds, states, n_jobs, c=1):
x, y = load_data(mat)
results = dict()
results["final"] = []
for i in range(number_folds):
test_index = list(range(i, len(x), number_folds))
validation_index = list(set(range(len(x))) - set(test_index))
# Loads and split data
x_v, y_v = np.array(x)[validation_index], np.array(y)[validation_index]
x_t, y_t = np.array(x)[test_index], np.array(y)[test_index]
# Does the validation
our_states, normal_states = validation(mat, x_v, y_v, dist, labels,
number_folds, states, n_jobs, c)
# Does the test
results["final"].append(
test(mat, our_states, normal_states, x_v, y_v, x_t, y_t, labels,
dist, n_jobs, c))
time.sleep(1)
write_out(mat, results, 0, c)
