new_train[:, i] = minmax(new_train[:, i])
#for plotting of polynomial curves - cotinuous data set over range of original data
#then add polynomial features and normalise
continuous_x = np.arange(1, 7.01, 0.01).reshape(-1, 1)
x_ = add_polynomial_features(continuous_x, 10)
for i in range(10):
x_[:, i] = minmax(x_[:, i])
thetas = np.ones(11).reshape(-1, 1)
cost_values = []
thetas_list = []
mlr = MLR(thetas, alpha=0.009, n_cycle=5000)
for degree in range(2, 11):
mlr.thetas = thetas[:degree + 1]
thetas_list.append(mlr.fit_(new_train[:, :degree], y_train))
cost_values.append(
mlr.mse_(y_train,
mlr.predict_(new_train[:, :degree])[1]))
i = 2
for elem in thetas_list:
mlr.thetas = elem
y_hat = mlr.predict_(x_[:, :i])[1]
plt.plot(continuous_x, y_hat, '--')
plt.title(str(degree))
plt.title(('degree = ' + str(i) + ' cost: ' + str(cost_values[i - 2])))
plt.plot(x_train, y_train, 'go')
plt.plot(x_test, y_test, 'ro')
plt.show()
data = pd.read_csv("../subjects/day01/resources/are_blue_pills_magics.csv")
Xpill = np.array(data["Micrograms"]).reshape(-1,1)
Yscore = np.array(data["Score"]).reshape(-1,1)
thetas = np.array([1, 1])
#plt.plot(Xpill, Yscore, 'o')
mlr = MyLR(thetas, alpha=0.05, n_cycle=5000)
#th = mlr.fit_(Xpill, Yscore)
#print(th)
#plt.plot(Xpill, (th[1] * Xpill + th[0]), '-r')
#plt.show()
for j in range(80, 100, 5):
res = []
for i in range(-11, -7, 1):
mlr.thetas = np.array([j, i])
dummy, y_hat = mlr.predict_(Xpill)
res.append(mlr.mse_(Yscore, y_hat))
np.array(res)
plt.plot(np.arange(-11, -7), res)
plt.show()
for j in range(80, 100, 5):
res = []
for i in range(-11, -7, 1):
mlr.thetas = np.array([j, i])
dummy, y_hat = mlr.predict_(Xpill)
res.append(mean_squared_error(Yscore, y_hat))
np.array(res)
plt.plot(np.arange(-11, -7), res, '-r')
plt.show()