for i in range(ln):
XS[i] = HT.dot(train_in[i])
return XS
def plot(x):
plt.plot([i for i in range(1,257)], x)
plt.show()
if __name__ == '__main__':
M = 12
train_in, train_out, test_in, test_out = loaddata()
#plot(train_in[101])
print train_in.shape
#print outputs.shape
knots = createknots(1,256)
print knots
p = len(train_in[0])
H = basismatrix(knots, p, M)
XS = transform(H, train_in, M)
print XS.shape
print np.linalg.matrix_rank(XS)
beta = lg.logistic(XS, train_out)
print beta
error = 0
TXS = transform(H, test_in, M)
for i in range(len(TXS)):
res = lg.predict(TXS[i], beta)
if test_out[i] != res:
error += 1
print 'error ratio:', float(error)/len(TXS)
row = [1]
for j in range(len(data[0])):
seg = []
if j == 3:
seg.append(data[i,j])
row = row + seg
continue
for k in range(0,K):
seg.append(N(data[i,j], k, knots[j]))
row = row + seg[1:]
new_matrix[i] = np.array(row)
return new_matrix
if __name__ == '__main__':
K = 5
X, Y = hd.loaddata()
knots = split(X, K)
print knots
H = cookdata(X, K, knots)
np.savetxt('H.dat', H, delimiter=',')
print H.shape
print np.linalg.matrix_rank(H)
beta = lg.logistic(H, Y)
print beta
error = 0
for i in range(len(H)):
res = lg.predict(H[i], beta)
if Y[i] != res:
error += 1
print 'error ratio:', float(error)/len(X)
