import sys
import numpy as np
import hw2_fc as fc
w = np.load("logisticw.npy")
xt = fc.readX(sys.argv[1])
xt = fc.fit(xt)
xt = np.array(xt)
xt = np.concatenate((np.ones((xt.shape[0], 1)), xt), axis=1)
yt = []
for i in range(len(xt)):
yt.append(fc.sign(fc.theta(np.dot(w, xt[i]))))
fc.outputcsv(yt, sys.argv[2])
x = np.array(x)
x = np.concatenate((np.ones((x.shape[0], 1)), x), axis=1)
y = np.array(y)
yval = np.array(yval)
xval = np.array(xval)
xval = np.concatenate((np.ones((xval.shape[0], 1)), xval), axis=1)
lr = 0.1
w = np.array([1.0] * len(x[0]))
s_gra = np.zeros(len(x[0]))
for iter in range(60):
for a in range(lx):
fx = fc.theta(np.dot(w, x[a]))
gra = (fx - y[a]) * x[a]
s_gra += gra**2
ada = np.sqrt(s_gra)
w -= lr * gra / ada
err = 0
for i in range(lx):
if fc.sign(fc.theta(np.dot(w, x[i]))) != y[i]: err += 1
errval = 0
for i in range(lxval):
if fc.sign(fc.theta(np.dot(w, xval[i]))) != yval[i]: errval += 1
print("Iteration = %d | Accuracyin = %f | Accruacyout = %f" %
(iter, 1 - err / lx, 1 - errval / lxval))
np.save("logisticw.npy", w)
pc = np.load("generative_pc.npy")
pxc = np.load("generative_pxc.npy")
xt = fc.readX(sys.argv[1])
lxt = len(xt)
mean = [0] * len(xt[0])
for feature in fc.noz:
for xs in range(lxt):
mean[feature] += xt[xs][feature] / lxt
yt = []
for i in range(lxt):
up, down = 1, 1
for feature in range(len(xt[i])):
if feature not in fc.noz:
up *= pxc[1][feature][xt[i][feature]]
down *= pxc[0][feature][xt[i][feature]]
else:
if xt[i][feature] > mean[feature]:
up *= pxc[1][feature][1]
down *= pxc[0][feature][1]
else:
up *= pxc[1][feature][0]
down *= pxc[0][feature][0]
up *= pc[1]
down *= pc[0]
yt.append(fc.sign(up / (up + down)))
fc.outputcsv(yt, sys.argv[2])