# neural networks from scratch with numpy

import numpy as np

def mean_absolute_error(x, y):
  return np.mean(np.abs(x - y))

def mean_squared_error(x, y):
  return np.mean(np.power(x - y, 2))

def cross_entropy_loss(x, y):
  return -np.log(np.exp(y) / np.sum(np.exp(x)))

# preapre inputs and outputs
x = np.array([[1, 0]])
y = np.array([[1]])

# we're doing xavier initialisation - see <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>
w1 = np.random.randn(2, 3) / np.sqrt(2)
w2 = np.random.randn(3, 1) / np.sqrt(3)

def single_pass():
  # forward pass
  h = np.matmul(x, w1)
  h_hat = np.tanh(h)
  j = np.matmul(h_hat, w2)
  print("prediction {}".format(j))

  # loss calculation
  loss = cross_entropy_loss(j, y)
  print("loss {}".format(loss))

  # TODO Backward pass.
  return

# initialise layers
#   self.lin1 = nn.Linear(2, 3)
#   self.lin2 = nn.Linear(3, 1)
#   self.loss = nn.MSELoss()
# and then
#   x = self.lin1(x)
#   x = F.relu(x)
#   x = self.lin2(x)
#   x = F.softmax(x)
#   loss = self.loss(x, y)

# TODO Add support for numpy matrices.
class Tensor:
  def __init__(self, value):
    self.value = value
    self.grad = 0
    # Required for backprop.
    self._parents = None
    self._back = None

  def __repr__(self) -> str:
    return f"Tensor(value={self.value}, grad={self.grad})"

  # Save values for the backward pass.
  def _save(self, *args):
    self._parents = args

  def add(self, other):
    tensor = Tensor(self.value +  other.value)
    tensor._save(self, other)

    def back(upstream):
      return upstream * 1, upstream * 1

    tensor._back = back
    return tensor

  def mul(self, other):
    tensor = Tensor(self.value * other.value)
    tensor._save(self, other)

    def back(upstream):
      a, b = tensor._parents
      return upstream * b.value, upstream * a.value

    tensor._back = back
    return tensor

  def expt(self, exponent):
    tensor = Tensor(self.value ** exponent)
    tensor._save(self)

    def back(upstream):
      a, = tensor._parents
      return [ upstream * exponent * (a.value ** (exponent - 1)) ]

    tensor._back = back
    return tensor

  def reciprocal(self):
    tensor = Tensor(1.0 / self.value)
    tensor._save(self)

    def back(upstream):
      a, = tensor._parents
      return [ -1.0 / (a.value ** 2) ]

    tensor._back = back
    return tensor

  def exp(self):
    tensor = Tensor(np.exp(self.value))
    tensor._save(self)

    def back(upstream):
      a, = tensor._parents
      return [ np.exp(a.value) ]

    tensor._back = back
    return tensor

  def _backprop(tensor, upstream):
    # Backprop through the tensor iff it has any parents.
    if tensor._parents is not None:
      for node, grad in zip(tensor._parents, tensor._back(upstream)):
        # Set the node gradient to the computed gradient.
        node.grad = grad
        # Iterate through all (possible) parent nodes of this node.
        node._backprop(grad)

  def backward(self):
    # Partial of self with respect to self is ALWAYS 1.
    self.grad = 1
    Tensor._backprop(self, self.grad)

def test():
  # Forward pass.
  x, y, z = Tensor(-2), Tensor(5), Tensor(-4)
  q = x.add(y)
  h = q.expt(2)
  w = h.mul(z)
  print(f"q = {q}, w = {w}")

  # Backward pass.
  w.backward()
  print(f"is: dw = {w.grad}, dz = {z.grad}, dy = {y.grad}, dx = {x.grad}")