"""

10 new features

Selecting the appropriate activation function for polymorphic neurons can be based on various heuristics. These heuristics should ideally capture meaningful aspects of the input data or the state of the network that inform the choice of the activation function. Here are some potential heuristics with associated pseudocode:

1. **Variance-Based Selection**:
   - **Description**: Choose the activation function based on the variance of the neuron's input. Higher variance might indicate a need for a more nonlinear activation function.
   - **Pseudocode**:
     ```python
     def variance_based_selection(input):
         variance = calculate_variance(input)
         if variance > high_variance_threshold:
             return nonlinear_activation_function
         else:
             return linear_activation_function
     ```

2. **Error-Driven Selection**:
   - **Description**: Select the activation function based on the current error or loss of the network. Different activation functions may be more effective at different stages of training or for different error magnitudes.
   - **Pseudocode**:
     ```python
     def error_driven_selection(current_error):
         if current_error > high_error_threshold:
             return robust_activation_function
         else:
             return efficient_activation_function
     ```

3. **Frequency-Domain Analysis**:
   - **Description**: Use a frequency-domain analysis of the input (e.g., using a Fourier transform) and select the activation function based on the dominant frequency components.
   - **Pseudocode**:
     ```python
     def frequency_domain_selection(input):
         frequency_components = compute_fourier_transform(input)
         dominant_frequency = find_dominant_frequency(frequency_components)
         if dominant_frequency > high_frequency_threshold:
             return high_frequency_activation_function
         else:
             return low_frequency_activation_function
     ```

4. **Gradient-Based Selection**:
   - **Description**: Choose the activation function based on the gradient of the loss with respect to the input. This could help in mitigating vanishing or exploding gradients.
   - **Pseudocode**:
     ```python
     def gradient_based_selection(gradient):
         if abs(gradient) > high_gradient_threshold:
             return activation_function_for_high_gradient
         else:
             return activation_function_for_low_gradient
     ```

5. **Historical Performance-Based Selection**:
   - **Description**: Select the activation function based on the historical performance of different activation functions for similar inputs or in similar network states.
   - **Pseudocode**:
     ```python
     def historical_performance_based_selection(input, historical_data):
         similar_case = find_similar_case(input, historical_data)
         best_performing_activation = similar_case.best_activation_function
         return best_performing_activation
     ```

6. **Input Distribution-Based Selection**:
   - **Description**: Choose the activation function based on the statistical distribution of the input data (e.g., skewness, kurtosis).
   - **Pseudocode**:
     ```python
     def input_distribution_based_selection(input):
         skewness = calculate_skewness(input)
         if skewness > skewness_threshold:
             return activation_function_for_skewed_data
         else:
             return default_activation_function
     ```

Each of these heuristics offers a different approach to dynamically selecting activation functions, potentially leading to more adaptive and effective neural network models. The choice of heuristic should be informed by the specific characteristics of the task and the nature of the input data.

"""
import torch
import torch.nn as nn
import torch.nn.functional as F


class PolymorphicNeuronLayer(nn.Module):
    def __init__(self, in_features, out_features, activation_functions):
        """
        Initialize the Polymorphic Neuron Layer.
        :param in_features: Number of input features.
        :param out_features: Number of output features (neurons).
        :param activation_functions: List of activation functions to choose from.

        Example:
        >>> x = torch.randn(1, 10)
        >>> neuron = PolymorphicNeuronLayer(in_features=10, out_features=5, activation_functions=[F.relu, F.tanh, F.sigmoid])
        >>> output = neuron(x)
        >>> output.shape
        """
        super(PolymorphicNeuronLayer, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.activation_functions = activation_functions
        self.weights = nn.Parameter(torch.randn(out_features, in_features))
        self.bias = nn.Parameter(torch.randn(out_features))

    def forward(self, x):
        """
        Forward pass of the layer.
        :param x: Input tensor.
        :return: Output tensor after applying polymorphic neurons.
        """
        # Linear transformation
        x = F.linear(x, self.weights, self.bias)

        # Apply activation function dynamically
        outputs = []
        for i in range(self.out_features):
            # Example criterion: Use mean of input for selecting activation function
            criterion = x[:, i].mean()
            activation_idx = int(criterion % len(self.activation_functions))
            activation_function = self.activation_functions[activation_idx]
            outputs.append(activation_function(x[:, i]))

        # Stack outputs along the feature dimension
        return torch.stack(outputs, dim=1)


# # Example usage
# polymorphic_layer = PolymorphicNeuronLayer(in_features=10, out_features=5, )

# # Example input
# input_tensor = torch.randn(1, 10)

# # Forward pass
# output = polymorphic_layer(input_tensor)
