'std recall': [],
'best_cf': []
}
results = {
'realization': [],
'ACCURACY': [],
'AUC': [],
'MCC': [],
'f1_score': [],
'precision': [],
'recall': [],
'cf': []
}
base = load_mock(type='LOGICAL_AND')
pos = base[:, :2][where(base[:, 2] == 1)[0]]
neg = base[:, :2][where(base[:, 2] == 0)[0]]
plt.plot(pos[:, 0], pos[:, 1], 'bo')
plt.plot(neg[:, 0], neg[:, 1], 'ro')
plt.show()
C = [0, 1]
for realization in range(20):
train, test = split_random(base, train_percentage=.8)
x_train = train[:, :2]
y_train = train[:, 2]
'best_cf': [],
'alphas': []
}
results = {
'realization': [],
'ACCURACY': [],
# 'MCC': [],
'f1_score': [],
'precision': [],
'recall': [],
'cf': [],
'alphas': []
}
base = load_mock(type='TRIANGLE_CLASSES')
# normalizar a base
base[['x1', 'x2']] = normalization(base[['x1', 'x2']], type='min-max')
x = array(base[['x1', 'x2']])
y = array(base[['y']])
classe0 = x[np.where(y == 0)[0]]
classe1 = x[np.where(y == 1)[0]]
classe2 = x[np.where(y == 2)[0]]
plt.plot(classe0[:, 0], classe0[:, 1], 'b^')
plt.plot(classe1[:, 0], classe1[:, 1], 'go')
plt.plot(classe2[:, 0], classe2[:, 1], 'm*')
plt.xlabel("X1")
plt.ylabel("X2")
'best_cf': [],
'alphas': []
}
results = {
'realization': [],
'ACCURACY': [],
# 'MCC': [],
'f1_score': [],
'precision': [],
'recall': [],
'cf': [],
'alphas': []
}
base = pd.DataFrame(load_mock(type='LOGICAL_XOR'),
columns=['x1', 'x2', 'y'])
base[['x1', 'x2']] = normalization(base[['x1', 'x2']], type='min-max')
x = array(base[['x1', 'x2']])
y = array(base[['y']])
classe0 = x[np.where(y == 0)[0]]
classe1 = x[np.where(y == 1)[0]]
plt.plot(classe0[:, 0], classe0[:, 1], 'b^')
plt.plot(classe1[:, 0], classe1[:, 1], 'go')
plt.xlabel("X1")
plt.ylabel("X2")
plt.savefig(get_project_root() + '/run/TR-05/XOR/results/' +
'dataset_xor_artificial.png')
from mlfwk.visualization import generate_space, coloring
if __name__ == '__main__':
print("run artificial seno")
final_result = {
'MSE': [],
'std MSE': [],
'RMSE': [],
'std RMSE': [],
'R2': [],
'std R2': []
}
results = {'realization': [], 'MSE': [], 'RMSE': [], 'R2': []}
base = load_mock(type='MOCK_SENO')
# normalizar a base
base[['x1']] = normalization(base[['x1']], type='min-max')
sn.set_style('whitegrid')
sn.scatterplot(data=base, x="x1", y="y", color='c')
plt.xlabel("X1")
plt.ylabel("Y")
plt.savefig(get_project_root() +
'/run/TR-05/ARTIFICIAL_REGRESSAO/results/' +
'dataset_seno_artificial.png')
plt.show()
x = array(base[['x1']])
y = array(base[['y']])
'MSE': [],
'std MSE': [],
'RMSE': [],
'std RMSE': [],
'alphas': []
}
results = {
'realization': [],
'MSE': [],
'RMSE': [],
'erros': [],
'alphas': []
}
F, x1, x2 = load_mock(type='2D_REGRESSOR')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x1, x2, F, color='k')
ax.set_title("y = ax1 + bx2 + c")
ax.set_xlabel("x1")
ax.set_ylabel("x2")
ax.set_zlabel("y")
plt.savefig(get_project_root() + '/run/TR-02/ARTIFICIAL/results/' +
'adaline_fig_2.jpg')
plt.show()
base = concatenate(
[array(x1, ndmin=2),
array(x2, ndmin=2),
'MSE': [],
'std MSE': [],
'RMSE': [],
'std RMSE': [],
'alphas': []
}
results = {
'realization': [],
'MSE': [],
'RMSE': [],
'erros': [],
'alphas': []
}
F, x = load_mock(type='LINEAR_REGRESSOR')
# plt.plot(x, F, 'bo', color='k')
# # plt.plot(array(x, ndmin=2).T, regressor_adaline.predict(array(x, ndmin=2).T))
# plt.xlabel('x')
# plt.ylabel('y')
# plt.savefig(get_project_root() + '/run/TR-02/ARTIFICIAL/results/' + 'adaline_fig_1.jpg')
# plt.show()
plt.style.use('default')
plt.style.use('ggplot')
fig, ax = plt.subplots(figsize=(8, 4))
# ax.plot(array(x, ndmin=2).T, regressor_adaline.predict(array(x, ndmin=2).T), color='k', label='g(x)')
ax.scatter(x,
