import numpy as np


class NeuralNetworkFiltering:
    """
    A comprehensive neural network-based filtering approach for adaptive signal processing.

    This class supports various neural network architectures, including feedforward, convolutional, and recurrent networks.
    It includes advanced features such as dropout for regularization, batch normalization for faster convergence,
    and customizable training options.

    Methods
    -------
    train : method
        Trains the neural network on the given signal.
    apply_filter : method
        Applies the trained neural network to filter the signal.
    evaluate : method
        Evaluates the performance of the neural network on a test signal.

    Example Usage
    -------------
    signal = np.sin(np.linspace(0, 10, 100)) + np.random.normal(0, 0.1, 100)
    nn_filter = NeuralNetworkFiltering(signal, network_type='feedforward', hidden_layers=[64, 64], epochs=100)

    # Train the neural network filter
    nn_filter.train()

    # Apply the trained filter
    filtered_signal = nn_filter.apply_filter()
    print("Filtered Signal:", filtered_signal)

    # Evaluate the filter on a test signal
    test_signal = np.sin(np.linspace(0, 10, 100)) + np.random.normal(0, 0.1, 100)
    mse = nn_filter.evaluate(test_signal)
    print("Mean Squared Error on Test Signal:", mse)
    """

    def __init__(
        self,
        signal,
        network_type="feedforward",
        hidden_layers=[64, 64],
        learning_rate=0.001,
        epochs=100,
        batch_size=32,
        dropout_rate=0.5,
        batch_norm=True,
        recurrent_type="lstm",
    ):
        """
        Initialize the NeuralNetworkFiltering class with the signal and neural network configuration.

        Parameters
        ----------
        signal : numpy.ndarray
            The signal to be processed.
        network_type : str, optional
            The type of neural network to use ('feedforward', 'convolutional', 'recurrent'), default is 'feedforward'.
        hidden_layers : list of int, optional
            The number of neurons in each hidden layer (applicable for feedforward network), default is [64, 64].
        learning_rate : float, optional
            The learning rate for training, default is 0.001.
        epochs : int, optional
            The number of training epochs, default is 100.
        batch_size : int, optional
            The batch size for training, default is 32.
        dropout_rate : float, optional
            The dropout rate for regularization, default is 0.5.
        batch_norm : bool, optional
            Whether to apply batch normalization, default is True.
        recurrent_type : str, optional
            The type of recurrent network ('lstm' or 'gru'), applicable for recurrent networks, default is 'lstm'.
        """
        self.signal = signal
        self.network_type = network_type
        self.hidden_layers = hidden_layers
        self.learning_rate = learning_rate
        self.epochs = epochs
        self.batch_size = batch_size
        self.dropout_rate = dropout_rate
        self.batch_norm = batch_norm
        self.recurrent_type = recurrent_type

        self.network = self._build_network()

    def _build_network(self):
        """
        Build the neural network based on the specified configuration.

        Returns
        -------
        NeuralNetwork
            The constructed neural network model.

        Raises
        ------
        ValueError
            If the specified network type is unknown.
        """
        if self.network_type == "feedforward":
            return FeedforwardNetwork(
                self.hidden_layers, self.dropout_rate, self.batch_norm
            )
        elif self.network_type == "convolutional":
            return ConvolutionalNetwork(self.dropout_rate, self.batch_norm)
        elif self.network_type == "recurrent":
            return RecurrentNetwork(
                self.recurrent_type, self.dropout_rate, self.batch_norm
            )
        else:
            raise ValueError(f"Unknown network type: {self.network_type}")

    def train(self):
        """
        Train the neural network on the given signal.

        The signal is divided into input-output pairs for supervised training.
        This method prepares the data, then trains the network using the specified learning rate,
        number of epochs, and batch size.
        """
        X_train, y_train = self._prepare_data(self.signal)
        self.network.train(
            X_train, y_train, self.learning_rate, self.epochs, self.batch_size
        )

    def apply_filter(self):
        """
        Apply the trained neural network to filter the signal.

        The trained neural network is used to predict and filter the input signal.

        Returns
        -------
        numpy.ndarray
            The filtered signal after applying the neural network.
        """
        X = self._prepare_input(self.signal)
        return self.network.predict(X)

    def evaluate(self, test_signal):
        """
        Evaluate the performance of the neural network on a test signal.

        The method calculates the Mean Squared Error (MSE) between the predicted and actual values of the test signal.

        Parameters
        ----------
        test_signal : numpy.ndarray
            The test signal to evaluate the neural network's performance.

        Returns
        -------
        float
            The mean squared error (MSE) of the neural network on the test signal.
        """
        X_test, y_test = self._prepare_data(test_signal)
        predictions = self.network.predict(X_test)
        mse = np.mean((predictions - y_test) ** 2)
        return mse

    def _prepare_data(self, signal):
        """
        Prepare input-output pairs from the signal for training.

        Parameters
        ----------
        signal : numpy.ndarray
            The signal to be processed.

        Returns
        -------
        tuple
            Input features (X) and corresponding outputs (y) for training.
        """
        X, y = [], []
        for i in range(len(signal) - 1):
            X.append(signal[i])
            y.append(signal[i + 1])
        return np.array(X).reshape(-1, 1), np.array(y)

    def _prepare_input(self, signal):
        """
        Prepare input features from the signal for prediction.

        Parameters
        ----------
        signal : numpy.ndarray
            The signal to be processed.

        Returns
        -------
        numpy.ndarray
            Input features (X) for prediction.
        """
        X = []
        for i in range(len(signal) - 1):
            X.append(signal[i])
        return np.array(X).reshape(-1, 1)


# Simple Feedforward Network Implementation (Placeholder)


class FeedforwardNetwork:
    def __init__(self, hidden_layers, dropout_rate, batch_norm):
        self.hidden_layers = hidden_layers
        self.dropout_rate = dropout_rate
        self.batch_norm = batch_norm

        input_size = 1  # Assuming input signal is 1D
        # Ensure the last layer has only one output neuron
        self.weights = (
            [np.random.randn(input_size, hidden_layers[0])]
            + [
                np.random.randn(n_in, n_out)
                for n_in, n_out in zip(hidden_layers[:-1], hidden_layers[1:])
            ]
            + [np.random.randn(hidden_layers[-1], 1)]
        )  # Output layer has 1 neuron
        self.biases = [np.random.randn(n_out) for n_out in hidden_layers] + [
            np.random.randn(1)
        ]  # Output layer bias

    def _forward(self, X):
        activations = [X]
        for W, b in zip(self.weights, self.biases):
            Z = np.dot(activations[-1], W) + b
            if self.batch_norm:
                Z = self._batch_normalization(Z)
            A = self._relu(Z)
            if self.dropout_rate > 0:
                A = self._apply_dropout(A)
            activations.append(A)
        return activations

    def _backward(self, X, y, activations, learning_rate):
        delta = activations[-1] - y.reshape(
            -1, 1
        )  # Fix the shape of y to match activations[-1]
        for i in reversed(range(len(self.weights))):
            dW = np.dot(activations[i].T, delta)
            db = np.sum(delta, axis=0)
            delta = np.dot(delta, self.weights[i].T) * self._relu_derivative(
                activations[i]
            )
            self.weights[i] -= learning_rate * dW
            self.biases[i] -= learning_rate * db

    def train(self, X, y, learning_rate, epochs, batch_size):
        for epoch in range(epochs):
            for start in range(0, len(X), batch_size):
                end = start + batch_size
                batch_X = X[start:end]
                batch_y = y[start:end]
                self._train_batch(batch_X, batch_y, learning_rate)

    def _train_batch(self, X, y, learning_rate):
        # Forward pass
        activations = self._forward(X)
        # Backward pass
        self._backward(X, y, activations, learning_rate)

    def predict(self, X):
        A = X
        for W, b in zip(self.weights, self.biases):
            Z = np.dot(A, W) + b
            if self.batch_norm:
                Z = self._batch_normalization(Z)
            A = self._relu(Z)
        return A

    def _batch_normalization(self, Z):
        mean = np.mean(Z, axis=0)
        variance = np.var(Z, axis=0)
        return (Z - mean) / np.sqrt(variance + 1e-8)

    def _relu(self, Z):
        return np.maximum(0, Z)

    def _relu_derivative(self, Z):
        return Z > 0

    def _apply_dropout(self, A):
        mask = np.random.binomial(1, 1 - self.dropout_rate, size=A.shape)
        return A * mask / (1 - self.dropout_rate)


# Simple Convolutional Network Implementation (Placeholder)


class ConvolutionalNetwork:
    def __init__(self, dropout_rate, batch_norm):
        self.dropout_rate = dropout_rate
        self.batch_norm = batch_norm

        # Assume the input shape is (batch_size, height, width)
        self.filters = [
            np.random.randn(3, 3) for _ in range(16)
        ]  # 16 filters of size 3x3

        # Adjust the weight initialization for the fully connected layers
        self.weights = [np.random.randn(144, 64), np.random.randn(64, 1)]
        self.biases = [np.random.randn(64), np.random.randn(1)]

    def _forward(self, X):
        # Apply convolution followed by flattening the output
        A = self._convolution(X)
        A = A.reshape(A.shape[0], -1)  # Flatten
        activations = [A]
        for W, b in zip(self.weights, self.biases):
            Z = np.dot(activations[-1], W) + b
            if self.batch_norm:
                Z = self._batch_normalization(Z)
            A = self._relu(Z)
            if self.dropout_rate > 0:
                A = self._apply_dropout(A)
            activations.append(A)
        return activations

    def train(self, X, y, learning_rate, epochs, batch_size):
        for epoch in range(epochs):
            for start in range(0, len(X), batch_size):
                end = start + batch_size
                batch_X = X[start:end]
                batch_y = y[start:end]
                self._train_batch(batch_X, batch_y, learning_rate)

    def _train_batch(self, X, y, learning_rate):
        # Forward pass
        activations = self._forward(X)
        # Backward pass
        self._backward(X, y, activations, learning_rate)

    def _backward(self, X, y, activations, learning_rate):
        delta = activations[-1] - y
        for i in reversed(range(len(self.weights))):
            dW = np.dot(activations[i].T, delta)
            db = np.sum(delta, axis=0)
            delta = np.dot(delta, self.weights[i].T) * self._relu_derivative(
                activations[i]
            )
            self.weights[i] -= learning_rate * dW
            self.biases[i] -= learning_rate * db

    def predict(self, X):
        A = self._convolution(X)
        A = A.reshape(A.shape[0], -1)  # Flatten for fully connected layers
        for W, b in zip(self.weights, self.biases):
            Z = np.dot(A, W) + b
            if self.batch_norm:
                Z = self._batch_normalization(Z)
            A = self._relu(Z)
        return A

    def _convolution(self, X):
        output = np.zeros(
            (X.shape[0], X.shape[1] - 2, X.shape[2] - 2, len(self.filters))
        )
        for i, f in enumerate(self.filters):
            for j in range(output.shape[1]):
                for k in range(output.shape[2]):
                    output[:, j, k, i] = np.sum(
                        X[:, j : j + 3, k : k + 3] * f, axis=(1, 2)
                    )
        return output

    def _batch_normalization(self, Z):
        mean = np.mean(Z, axis=0)
        variance = np.var(Z, axis=0)
        return (Z - mean) / np.sqrt(variance + 1e-8)

    def _relu(self, Z):
        return np.maximum(0, Z)

    def _relu_derivative(self, Z):
        return Z > 0

    def _apply_dropout(self, A):
        mask = np.random.binomial(1, 1 - self.dropout_rate, size=A.shape)
        return A * mask / (1 - self.dropout_rate)


# Simple Recurrent Network Implementation (Placeholder)
class RecurrentNetwork:
    def __init__(self, recurrent_type="lstm", dropout_rate=0.5, batch_norm=True):
        self.recurrent_type = recurrent_type
        self.dropout_rate = dropout_rate
        self.batch_norm = batch_norm

        input_size = 10  # Adjust the input size based on expected input shape
        self.Wx = np.random.randn(input_size, 64)  # Input-to-hidden weight
        self.Wh = np.random.randn(64, 64)  # Hidden-to-hidden weight
        self.b = np.random.randn(64)  # Bias for the hidden layer
        self.Wy = np.random.randn(64, 1)  # Output weight
        self.by = np.random.randn(1)  # Bias for the output

    def _backward(self, X, y, activations, learning_rate):
        delta = activations[-1] - y.reshape(-1, 1)  # Fix the shape mismatch
        dWy = np.dot(activations[0].T, delta)
        dby = np.sum(delta, axis=0)
        self.Wy -= learning_rate * dWy
        self.by -= learning_rate * dby

    def train(self, X, y, learning_rate, epochs, batch_size):
        for epoch in range(epochs):
            for start in range(0, len(X), batch_size):
                end = start + batch_size
                batch_X = X[start:end]
                batch_y = y[start:end]
                self._train_batch(batch_X, batch_y, learning_rate)

    def _train_batch(self, X, y, learning_rate):
        # Forward pass
        activations = self._forward(X)
        # Backward pass
        self._backward(X, y, activations, learning_rate)

    def _forward(self, X):
        h = np.zeros((X.shape[0], 64))  # Initial hidden state
        if self.recurrent_type == "lstm":
            h, _ = self._lstm(X, h)
        elif self.recurrent_type == "gru":
            h = self._gru(X, h)
        Z = np.dot(h, self.Wy) + self.by
        return [h, Z]

    def predict(self, X):
        h = np.zeros((X.shape[0], 64))
        if self.recurrent_type == "lstm":
            h, _ = self._lstm(X, h)
        elif self.recurrent_type == "gru":
            h = self._gru(X, h)
        Z = np.dot(h, self.Wy) + self.by
        return Z

    def _lstm(self, X, h):
        c = np.zeros_like(h)
        for t in range(X.shape[1]):
            f = self._sigmoid(np.dot(X[:, t], self.Wx) + np.dot(h, self.Wh) + self.b)
            i = self._sigmoid(np.dot(X[:, t], self.Wx) + np.dot(h, self.Wh) + self.b)
            o = self._sigmoid(np.dot(X[:, t], self.Wx) + np.dot(h, self.Wh) + self.b)
            c = f * c + i * np.tanh(np.dot(X[:, t], self.Wx) + self.b)
            h = o * np.tanh(c)
        return h, c

    def _gru(self, X, h):
        for t in range(X.shape[1]):
            z = self._sigmoid(np.dot(X[:, t], self.Wx) + np.dot(h, self.Wh) + self.b)
            r = self._sigmoid(np.dot(X[:, t], self.Wx) + np.dot(h, self.Wh) + self.b)
            h_tilde = np.tanh(
                np.dot(X[:, t], self.Wx) + np.dot(r * h, self.Wh) + self.b
            )
            h = (1 - z) * h + z * h_tilde
        return h

    def _sigmoid(self, Z):
        return 1 / (1 + np.exp(-Z))

    def _batch_normalization(self, Z):
        mean = np.mean(Z, axis=0)
        variance = np.var(Z, axis=0)
        return (Z - mean) / np.sqrt(variance + 1e-8)

    def _relu(self, Z):
        return np.maximum(0, Z)

    def _relu_derivative(self, Z):
        return Z > 0

    def _apply_dropout(self, A):
        mask = np.random.binomial(1, 1 - self.dropout_rate, size=A.shape)
        return A * mask / (1 - self.dropout_rate)
