from __future__ import absolute_import
from __future__ import print_function
from builtins import range
import autograd.numpy as np
from autograd import grad
from autograd.test_util import check_grads

def sigmoid(x):
    return 0.5*(np.tanh(x) + 1)

def logistic_predictions(weights, inputs):
    # Outputs probability of a label being true according to logistic model.
    return sigmoid(np.dot(inputs, weights))

def training_loss(weights):
    # Training loss is the negative log-likelihood of the training labels.
    preds = logistic_predictions(weights, inputs)
    label_probabilities = preds * targets + (1 - preds) * (1 - targets)
    return -np.sum(np.log(label_probabilities))

# Build a toy dataset.
# inputs = np.array([[0.52, 1.12,  0.77],
#                    [0.88, -1.08, 0.15],
#                    [0.52, 0.06, -1.30],
#                    [0.74, -2.49, 1.39]])
# targets = np.array([True, True, False, True])

M = 5 
N = 30 
inputs = np.random.randn(N, M)
targets = np.random.randint(0,2,N).astype(bool)

print(inputs.shape)
print(targets.shape)

# Build a function that returns gradients of training loss using autograd.
training_gradient_fun = grad(training_loss)

# Check the gradients numerically, just to be safe.
weights = np.zeros(M)
check_grads(training_loss, modes=['rev'])(weights)

# Optimize weights using gradient descent.
print("Initial loss:", training_loss(weights))
for i in range(1000):
    weights -= training_gradient_fun(weights) * 0.01

print("Trained loss:", training_loss(weights))