#ifndef ACTIVATION_FUNCTION_H
#define ACTIVATION_FUNCTION_H

#include <algorithm>
#include <cmath>
#include <vector>
/**
 * Functor to set the activation function as a Sigmoid function
 */
struct Sigmoid {
  void static apply(std::vector<float> &z) {
    for (size_t i = 0; i < z.size(); i++) {
      z[i] = 1 / (1 + exp(-z[i]));
    };
  };
  float static init_stddev(int n) { return sqrt(1.0 / n); };
  float static derivative(float x) {
    float exp_x = exp(-x);
    return exp_x / pow(exp_x + 1.0, 2.0);
  }
};

/**
 * Functor to set the activation function as Rectified Linear Unit
 */
struct ReLU {
  void static apply(std::vector<float> &z) {
    for (size_t i = 0; i < z.size(); i++) {
      z[i] = std::max(0.0f, z[i]);
    };
  };
  float static init_stddev(int n) { return sqrt(2.0 / n); };
  float static derivative(float x) {
    if (x < 0) {
      return 0;
    } else {
      return 1;
    }
  };
};

/**
 * SoftMax Activation function.
 * This is generally used in the final output layer
 */
struct SoftMax {
  void static apply(std::vector<float> &z) {
    float zmax = *std::max_element(z.begin(), z.end());
    float sum = 0.0;
    for (size_t i = 0; i < z.size(); i++) {
      z[i] = exp(z[i] - zmax);
      sum += z[i];
    };
    for (size_t i = 0; i < z.size(); i++) {
      z[i] = z[i] / sum;
    };
  };
  float init_stddev(int n) { return sqrt(1.0 / n); };
};

#endif