def choose_gradient():
print("********************************************")
print("*******Choose your gradient technique*******")
print("*******************Linear*******************")
print("********************************************")
print("(1): Gradient Descent (2): Mini-batches (3): Stochastic")
technique = 0
while technique not in range(1, 4):
technique = int(input("Which one? "))
if technique == 1:
print("Starting Gradient Descent....")
time.sleep(5)
gradient_descent.run()
elif technique == 2:
print("Starting Mini-batches....")
time.sleep(5)
mini_batches.run()
elif technique == 3:
print("Starting Stochastic...")
time.sleep(5)
stochastic.run()
else:
print("Choose a valid number")
def main(): #main driver function
init_points = np.array([0.0, 1.0]) #intial points
print("2nd order optimization starts at " +
str(time.asctime())) #start time
time_t = time.time() #start time
newton_points = compute_newton(init_points,
max_iter=100) #find the solution
print(newton_points)
print("b = {0}, m = {1}, error = {2}".format(
newton_points[1], newton_points[0],
compute_total_error(newton_points[0], newton_points[1])))
time_t = time.time() - time_t #end time
print("2nd order optimization ends at %s and has taken %dms" %
(str(time.asctime()), time_t))
plot_line_data(newton_points[0],
newton_points[1]) #plot the line generated
print("1st order optimization starts at " +
str(time.asctime())) #start time
time_t = time.time()
m, b = gd.run()
time_t = time.time() - time_t #end time
print("1st order optimization ends at %s and has taken %dms" %
(str(time.asctime()), time_t))
plot_line_data(m, b) #plot the generated line
return
subplot.plot(x, y, 'bo', clip_on=False)
x = np.linspace(-2.0, 2.0, 100)
y = ne.evaluate('m*x + b', local_dict={'x': x, 'm': m, 'b': b})
subplot.plot(x, y, 'r')
subplot.grid()
plt.ylim([-15, 15])
plt.xlim([-2, 2])
plt.savefig(out_file, bbox_inches='tight', pad_inches=0.0)
data_points = np.load('example_1_sample_points.npy')
parameters = gradient_descent.run(data_points, initialize_params,
calculate_derivs, calculate_error)
print('plotting error surface and steps...(this could take a few minutes)')
count = 0
for i in range(0, len(parameters)):
error_fig = plt.figure()
error_surface = error_fig.add_subplot(111, projection='3d')
#Plot original error surface
plot_error_surface(error_surface)
error_surface.set_xlabel('m')
error_surface.set_ylabel('b')
error_surface.set_zlabel('Error')
error_surface.view_init(elev=40, azim=79)
err = 0
for j in range(0, i + 1):
y = point[0]
x = point[1]
subplot.plot(x, y, "bo", clip_on=False)
min_y = min(y, min_y)
max_y = max(y, max_y)
x = np.linspace(-2.5, 2.5, 100)
y = ne.evaluate("a + b*x + c*x**2 + d*x**3", local_dict={"x": x, "a": a, "b": b, "c": c, "d": d})
subplot.plot(x, y, "r")
plt.ylim([min_y, max_y])
plt.xlim([-2.5, 2.5])
plt.savefig(out_file, bbox_inches="tight", pad_inches=0.0)
data_points = np.load("example_2_sample_points.npy")
parameters = gradient_descent.run(
data_points, initialize_params, calculate_derivs, calculate_error, learning_rate=0.03, max_steps=100
)
print("plotting line graphs")
count = 0
for params in parameters:
param = params[0]
err = params[1]
save_line_graph(param, err, data_points, "line_graph-" + str(count) + ".png")
count = count + 1
np.save("parameter_estimates_example_2.npy", parameters)
def calculate_derivs(datas, params):
a, b, c, d = params[0], params[1], params[2], params[3]
derivs = [0, 0, 0, 0]
long = len(datas.iloc[:, 0])
for point in range(long):
y = datas.iloc[:, 2]
x = datas.iloc[:, 0]
inner = y - (a + b * x + c * x ** 2 + d * x ** 3)
derivs[0] = derivs[0] + inner
derivs[1] = derivs[1] + x * inner
derivs[2] = derivs[2] + (x ** 2) * inner
derivs[3] = derivs[3] + (x ** 3) * inner
factor = -2.0 / len(datas.iloc[:, 0])
for i in range(0, len(derivs)):
derivs[i] = factor * derivs[i]
return derivs
datas = pd.read_csv('/home/melis/Desktop/cwurData.csv')
parameters = gradient_descent.run(datas, initialize_params, calculate_derivs, learning_rate=0.03,
max_steps=30)
x = datas.iloc[:, 0]
plt.scatter(x, datas.iloc[:, 2])
y = parameters[0] + parameters[1] * x + parameters[2] * (x ** 2) + parameters[3] * (x ** 3)
plt.plot(x, y)
plt.show()
'a': a,
'b': b,
'c': c,
'd': d
})
subplot.plot(x, y, 'r')
plt.ylim([min_y, max_y])
plt.xlim([-2.5, 2.5])
plt.savefig(out_file, bbox_inches='tight', pad_inches=0.0)
data_points = np.load('example_2_sample_points.npy')
parameters = gradient_descent.run(data_points,
initialize_params,
calculate_derivs,
calculate_error,
learning_rate=0.03,
max_steps=100)
print('plotting line graphs')
count = 0
for params in parameters:
param = params[0]
err = params[1]
save_line_graph(param, err, data_points,
'line_graph-' + str(count) + '.png')
count = count + 1
np.save('parameter_estimates_example_2.npy', parameters)