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)
if outputs[i] == 1:
sbp_chd.append(inputs[i, 0])
else:
sbp_no_chd.append(inputs[i, 0])
return np.array(sbp_chd), np.array(sbp_no_chd)
def weight(x, xi, t):
return np.exp(-(x-xi)*(x-xi)/(2*t))
def parzen(x0, X):
s = 0.
for i in range(len(X)):
s += weight(x0, X[i], 30)
return s/(len(X)*30)
def draw(sbp_chd, sbp_no_chd):
predictors = np.arange(100., 220., 0.5)
responses = np.zeros(len(predictors))
for i in range(len(predictors)):
responses[i] = parzen(predictors[i], sbp_chd)
plt.plot(predictors, responses)
for i in range(len(predictors)):
responses[i] = parzen(predictors[i], sbp_no_chd)
plt.plot(predictors, responses)
if __name__ == '__main__':
inputs, outputs = CHD.loaddata()
sbp_chd, sbp_no_chd = cookdata(inputs, outputs)
draw(sbp_chd, sbp_no_chd)
plt.show()