def train(self, x, y):
"""
x - a list of x values
y - a list of y values
"""
x_mean = mean(x)
y_mean = mean(y)
x_dev = sum([abs(i - x_mean) for i in x])
y_dev = sum([abs(i - y_mean) for i in y])
self.slope = (x_dev * y_dev) / (x_dev * x_dev)
self.intercept = y_mean - (self.slope * x_mean)
def train(self, x, y):
"""
x - a list of x values
y - a list of y values
"""
x_mean = mean(x)
y_mean = mean(y)
x_dev = sum([abs(i - x_mean) for i in x])
y_dev = sum([abs(i - y_mean) for i in y])
self.slope = (x_dev * y_dev) / (x_dev * x_dev)
self.intercept = y_mean - (self.slope * x_mean)
def mean(A, ax=None):
if isinstance(A, (int, long, float, np.ndarray)):
return np.mean(A, ax)
elif isinstance(A, f.frac):
return f.mean(A)
elif isinstance(A, p.Poly):
return p.mean(A)
raise NotImplementedError
def mean(A, ax=None):
if isinstance(A, (int, long, float, np.ndarray)):
return np.mean(A, ax)
elif isinstance(A, f.frac):
return f.mean(A)
elif isinstance(A, p.Poly):
return p.mean(A)
raise NotImplementedError
class LinregNonMatrix(Algorithm):
def train(self, x, y):
x_mean = mean(x)
endfunction
function y = vandermonde3(a)
n = length(a)
y = kron(a, ones(1, n)).^kron([0:n-1]', ones(1, n))'
endfunction
from base import Algorithm, mean
class LinregNonMatrix(Algorithm):
def train(self, x, y):
x_mean = mean(x)
y_mean = mean(y)
x_dev = sum([i-x_mean for i in x])
y_dev = sum([i-y_mean for i in y])
self.slope = (x_dev*y_dev)/(x_dev*x_dev)
self.intercept = y_mean - (self.slope*x_mean)
def predict(self, x):
return [i*self.slope + self.intercept for i in x]
class LinregListMatrix(Algorithm):
Xrwm = base.rwm(post, N * N_temp, sigma_is[0], x0[:, 0],
Disp=0) #random walk metropolis
yy, ww = base.weight_samples(Xw, W_IS)
xx = base.resample_IS(yy, ww, N)
mean_r[i, :] = np.mean(Xrwm[N_temp * Burn_in:], 0)
mean_pf[i, :] = np.mean(Xptf[Burn_in:, :, 0], 0)
mean_sd[i, :] = np.mean(Xsd[Burn_in:, :, 0], 0)
mean_uw[i, :] = np.mean(Xuw[Burn_in:, :, 0], 0)
mean_w[i, :] = np.average(yy[Burn_in * math.factorial(N_temp):, :, 0],
0,
weights=ww[Burn_in * math.factorial(N_temp):])
mean_y[i, :] = np.mean(xx[Burn_in:, :, 0], 0)
mean_z[i, :] = base.mean(Xw[Burn_in:, :, :], W_IS[Burn_in:, :])
np.save('mean_r_circle_b.npy', mean_r)
np.save('mean_p_circle_b.npy', mean_p)
np.save('mean_uw_circle_b.npy', mean_uw)
np.save('mean_w_circle_b.npy', mean_w)
np.save('mean_sd_circle_b.npy', mean_sd)
print('----------------------------------')
print('----------------------------------')
print('means')
print(np.mean(mean_r, 0))
print(np.mean(mean_pf, 0))
print(np.mean(mean_sd, 0))
