def func(T, *args):
t = T.reshape((26, 26))
dataX = args[0]
dataY = args[1]
W = args[2]
return prob_grad.compute_log_p(dataX, dataY, W, t)
def func(W, *args): w = W.reshape(26, 128) dataX = args[0] dataY = args[1] T = args[2] return prob_grad.compute_log_p(dataX, dataY, w, T)
def func(params, *args):
#computes function value for a single example
W, T = params[:26*129].reshape((26, 129)),\
params[26*129:].reshape((26, 26))
x, y = args[0]
l = args[1]
log_p = prob_grad.compute_log_p(x, y, W, T)
return -1*log_p + 0.5*l*(\
np.sum(np.square(W)) +\
np.sum(np.square(T)))
def func(params, *args):
#computes function value for a single example
W, T = params[:26*129].reshape((26, 129)),\
params[26*129:].reshape((26, 26))
data = args[0]
l = args[1]
log_p = 0
for example in data:
log_p += prob_grad.compute_log_p(example[0], example[1], W, T)
return -1*log_p/len(data) + 0.5*l*(\
np.sum(np.square(W)) +\
np.sum(np.square(T)))
def func(params, *args):
#objective function specified in the handout
W, T = params[:26 * 128].reshape((26, 128)), params[26 * 128:].reshape(
(26, 26))
data = args[0]
C = args[1]
log_sum = 0
for example in data:
log_sum += prob_grad.compute_log_p(example[0], example[1], W, T)
norm = numpy.zeros(26)
for i in range(26):
norm[i] = numpy.linalg.norm(W[i])
numpy.square(norm, out=norm)
return -1 * (C / len(data)) * log_sum + 0.5 * numpy.sum(
norm) + 0.5 * numpy.sum(numpy.square(T))
import numpy, data_read, prob_grad
W, T = data_read.read_model()
data = data_read.read_train()
import time
t0 = time.time()
a = []
for example in data:
a.append(prob_grad.compute_log_p(example[0], example[1], W, T))
print(f"AVG: {sum(a)/len(data)}")
t1 = time.time()
print(f"Time: {t1-t0}")