def print_confusion(sinal_predicted_matrix,
labels_signals,
labels_model,
no_numbers=False,
norm=True):
print(sinal_predicted_matrix)
plot_confusion_matrix(sinal_predicted_matrix,
labels_signals,
labels_model,
no_numbers,
norm=norm)
def print_confusion(sinal_predicted_matrix,
labels_signals,
labels_model,
no_numbers=False):
print(sinal_predicted_matrix)
# cmap = make_cmap(get_color(), max_colors=1000)
plot_confusion_matrix(sinal_predicted_matrix,
labels_signals,
labels_model,
no_numbers,
norm=True) # , cmap=cmap)
def print_mean_loss(Mod, Sig, loss_tensor, signals_models, signals_tests):
labels_model = np.asarray(np.zeros(len(Mod) * 2, dtype=np.str), dtype=np.object)
labels_signals = np.asarray(np.zeros(len(Sig) * 2, dtype=np.str), dtype=np.object)
labels_model[list(range(1, len(Mod) * 2, 2))] = [signals_models[i]["s_name"] for i in Mod]
labels_signals[list(range(1, len(Sig) * 2, 2))] = [signals_tests[i][-1] for i in Sig]
mean_values_matrix = np.mean(loss_tensor, axis = 2)
sinal_predicted_matrix = np.zeros(len(Sig))
# for i in range(np.shape(sinal_predicted_matrix)[0]):
for j in range(np.shape(sinal_predicted_matrix)[0]):
sinal_predicted_matrix[j] = mean_values_matrix[0, j]
print(sinal_predicted_matrix)
# cmap = make_cmap(get_color(), max_colors=1000)
plot_confusion_matrix(sinal_predicted_matrix.T, labels_model, labels_signals) # , cmap=cmap)
def print_confusion(Mod, Sig, loss_tensor, signals_models, signals_tests):
labels_model = np.asarray(np.zeros(len(Mod) * 2, dtype=np.str),
dtype=np.object)
labels_signals = np.asarray(np.zeros(len(Sig) * 2, dtype=np.str),
dtype=np.object)
labels_model[list(range(1,
len(Mod) * 2,
2))] = [signals_models[i]["s_name"] for i in Mod]
labels_signals[list(range(1,
len(Sig) * 2,
2))] = [signals_tests[i][-1] for i in Sig]
predicted_matrix = np.argmin(loss_tensor[Mod][:, Sig, :], axis=0)
sinal_predicted_matrix = np.zeros((len(Sig), len(Mod)))
for i in range(np.shape(sinal_predicted_matrix)[0]):
for j in range(np.shape(sinal_predicted_matrix)[1]):
sinal_predicted_matrix[i, j] = sum(predicted_matrix[i, :] == j)
print(sinal_predicted_matrix)
# cmap = make_cmap(get_color(), max_colors=1000)
plot_confusion_matrix(sinal_predicted_matrix, labels_model,
labels_signals) # , cmap=cmap)
