<reponame>jleni/EPG
import chainer as C
import chainer.functions as F
import numpy as np


class NN(object):
    """Simple MLP
    """

    def __init__(self, dims=None, out_fn=None):
        assert dims is not None
        assert out_fn is not None
        assert len(dims) >= 2

        self._out_fn = out_fn
        self._lst_w, self._lst_b = [], []
        for i in range(len(dims) - 1):
            shp = dims[i + 1], dims[i]
            # Correctly init weights.
            std = 0.01 if i == len(dims) - 2 else 1.0
            out = np.random.randn(*shp).astype(np.float32)
            out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True))
            self._lst_w.append(C.Variable(out))
            self._lst_b.append(C.Variable(np.zeros(shp[0], dtype=np.float32)))
        self.train_vars = self._lst_w + self._lst_b

    def f(self, x):
        for i, (w, b) in enumerate(zip(self._lst_w, self._lst_b)):
            x = F.linear(x, w, b)
            if i != len(self._lst_w) - 1:
                x = F.tanh(x)
            else:
                return self._out_fn(x)

    def set_params_1d(self, params):
        """Set params for ES (theta)
        """
        n = self._lst_w + self._lst_b
        idx = 0
        for e in n:
            e.data[...] = params[idx:idx + e.size].reshape(e.shape)
            idx += e.size

    def get_params_1d(self):
        """Get params for ES (theta)
        """
        n = self._lst_w + self._lst_b
        return np.concatenate([e.data.flatten() for e in n])


class Memory(object):
    """Linear memory, allows storing information in its weights.
    """

    def __init__(self, size_in=None, size_out=None):
        assert size_in is not None
        assert size_out is not None

        shp = size_out, size_in
        # Correctly init weights.
        out = np.random.randn(*shp).astype(np.float32)
        out *= 0.01 / np.sqrt(np.square(out).sum(axis=0, keepdims=True))
        self._w = C.Variable(out)
        self._b = C.Variable(np.zeros(shp[0], dtype=np.float32))
        self.train_vars = [self._w, self._b]

    def f(self):
        x = np.ones(self._w.shape[1], dtype=np.float32)[np.newaxis, :]
        # Needs nonlinearity after memory
        return F.tanh(F.linear(x, self._w, self._b))
