def fit_model(data):
x = np.linspace(0, len(get_x_values(data)), len(get_x_values(data)))
y = get_y_values(data)
# calculate polynomial
z = np.polyfit(x, y, 4)
f = np.poly1d(z)
return f
def fit_model(data):
x = np.linspace(0, len(get_x_values(data)), len(get_x_values(data)))
y = get_y_values(data)
# calculate polynomial
z = np.polyfit(x, y, 4)
f = np.poly1d(z)
return f
def predict(f, data):
x_new = np.linspace(0, len(get_x_values(data)), len(get_x_values(data)))
y_new = f(x_new)
return y_new
if __name__ == '__main__':
data = read_data(sys.argv[1])
training_data = get_training_data(data)
print("fitting to %d rows" % training_data.shape[0])
model_subset = fit_model(training_data)
fitted_subset_y_values = predict(model_subset, data)
model_all = fit_model(data)
fitted_all_y_values = predict(model_all, data)
plt.plot(range(len(get_x_values(data))), get_y_values(data), 'go',
range(len(get_x_values(training_data))), get_y_values(training_data), 'ro',
range(len(get_x_values(data))), fitted_all_y_values, 'b',
range(len(get_x_values(data))), fitted_subset_y_values, 'pink')
plt.show()
def fit_model(data, default_params):
x = get_x_range(data)
y = get_y_values(data)
return minimize(fcn2min, default_params, args=(x, y)).params
