def cv_loo(x, t):
mistake = 0
k = len(x)
while len(x) != 2:
o = random.randint(0, len(x) - 1)
bad_x = x[o]
x = np.delete(x, o, 0)
t1 = np.ndarray(shape=(len(x), 1))
t2 = np.ndarray(shape=(len(x), 1))
for i in range(len(x)):
t1[i, 0] = x[:, 0][i]
t2[i, 0] = x[:, 1][i]
if t == 0:
p = rg.linear_regression(t1, t2)
elif t == 1:
p = rg.polynomial_regression(t1, t2, 10)
else:
p = rg.functional_regression(t1, t2, [f1, f2, f3, f4, f5, f6, f7, f8])
mistake += math.fabs(bad_x[1] - p(bad_x[0]))
return mistake / k
def cv_loo(x, t):
mistake = 0
k = len(x)
while len(x) != 2:
o = random.randint(0, len(x) - 1)
bad_x = x[o]
x = np.delete(x, o, 0)
t1 = np.ndarray(shape=(len(x), 1))
t2 = np.ndarray(shape=(len(x), 1))
for i in range(len(x)):
t1[i, 0] = x[:, 0][i]
t2[i, 0] = x[:, 1][i]
if t == 0:
p = rg.linear_regression(t1, t2)
elif t == 1:
p = rg.polynomial_regression(t1, t2, 10)
else:
p = rg.functional_regression(t1, t2,
[f1, f2, f3, f4, f5, f6, f7, f8])
mistake += math.fabs(bad_x[1] - p(bad_x[0]))
return mistake / k
def cv_k(x, t):
mistake = 0
bad_x = np.array_split(x, 20)
for j in range(20):
sum = 0
x1 = diff(x, bad_x[j])
t1 = np.ndarray(shape=(len(x1), 1))
t2 = np.ndarray(shape=(len(x1), 1))
for i in range(len(x1)):
t1[i, 0] = x1[:, 0][i]
t2[i, 0] = x1[:, 1][i]
if t == 0:
p = rg.linear_regression(t1, t2)
elif t == 1:
p = rg.polynomial_regression(t1, t2, 10)
else:
p = rg.functional_regression(t1, t2, [f1, f2, f3, f4, f5, f6, f7, f8])
for data in bad_x[j]:
sum += math.fabs(data[1] - p(data[0]))
sum /= len(x1[1])
mistake += sum
return mistake / 20
def cv_k(x, t):
mistake = 0
bad_x = np.array_split(x, 20)
for j in range(20):
sum = 0
x1 = diff(x, bad_x[j])
t1 = np.ndarray(shape=(len(x1), 1))
t2 = np.ndarray(shape=(len(x1), 1))
for i in range(len(x1)):
t1[i, 0] = x1[:, 0][i]
t2[i, 0] = x1[:, 1][i]
if t == 0:
p = rg.linear_regression(t1, t2)
elif t == 1:
p = rg.polynomial_regression(t1, t2, 10)
else:
p = rg.functional_regression(t1, t2,
[f1, f2, f3, f4, f5, f6, f7, f8])
for data in bad_x[j]:
sum += math.fabs(data[1] - p(data[0]))
sum /= len(x1[1])
mistake += sum
return mistake / 20
