// ext/convolver/convolver.c

#include <ruby.h>
#include "narray.h"
#include <stdio.h>
#include <xmmintrin.h>

#include "narray_shared.h"
#include "convolve_raw.h"
#include "cnn_components.h"

////////////////////////////////////////////////////////////////////////////////////////////////////

// To hold the module object
VALUE Convolver = Qnil;

static VALUE narray_fit_backwards( VALUE self, VALUE a, VALUE b ) {
  struct NARRAY *na_a, *na_b;
  volatile VALUE val_a, val_b;
  int target_rank, i;
  int shift_by[LARGEST_RANK];

  val_a = na_cast_object(a, NA_SFLOAT);
  GetNArray( val_a, na_a );

  val_b = na_cast_object(b, NA_SFLOAT);
  GetNArray( val_b, na_b );

  if ( na_a->rank != na_b->rank ) {
    rb_raise( rb_eArgError, "narray a must have equal rank to narray b (a rank %d, b rank %d)", na_a->rank,  na_b->rank );
  }

  if ( na_a->rank > LARGEST_RANK ) {
    rb_raise( rb_eArgError, "exceeded maximum narray rank for convolve of %d", LARGEST_RANK );
  }

  target_rank = na_a->rank;

  for ( i = 0; i < target_rank; i++ ) {
    if ( ( na_a->shape[i] - na_b->shape[i] ) < 0 ) {
      rb_raise( rb_eArgError, "no space for backward fit" );
    }
    shift_by[i] = na_b->shape[i] >> 1;
  }

  fit_backwards_raw(
    target_rank,
    na_a->shape, (float*) na_a->ptr,
    na_b->shape, (float*) na_b->ptr,
    shift_by );

  return Qnil;
}


/* @overload convolve( signal, kernel )
 * Calculates convolution of an array of floats representing a signal, with a second array representing
 * a kernel. The two parameters must have the same rank. The output has same rank, its size in each dimension d is given by
 *  signal.shape[d] - kernel.shape[d] + 1
 * @param [NArray] signal must be same size or larger than kernel in each dimension
 * @param [NArray] kernel must be same size or smaller than signal in each dimension
 * @return [NArray] result of convolving signal with kernel
 */
static VALUE narray_convolve( VALUE self, VALUE a, VALUE b ) {
  struct NARRAY *na_a, *na_b, *na_c;
  volatile VALUE val_a, val_b, val_c;
  int target_rank, i;
  int target_shape[LARGEST_RANK];

  val_a = na_cast_object(a, NA_SFLOAT);
  GetNArray( val_a, na_a );

  val_b = na_cast_object(b, NA_SFLOAT);
  GetNArray( val_b, na_b );

  if ( na_a->rank != na_b->rank ) {
    rb_raise( rb_eArgError, "narray a must have equal rank to narray b (a rack %d, b rank %d)", na_a->rank,  na_b->rank );
  }

  if ( na_a->rank > LARGEST_RANK ) {
    rb_raise( rb_eArgError, "exceeded maximum narray rank for convolve of %d", LARGEST_RANK );
  }

  target_rank = na_a->rank;

  for ( i = 0; i < target_rank; i++ ) {
    target_shape[i] = na_a->shape[i] - na_b->shape[i] + 1;
    if ( target_shape[i] < 1 ) {
      rb_raise( rb_eArgError, "narray b is bigger in one or more dimensions than narray a" );
    }
  }

  val_c = na_make_object( NA_SFLOAT, target_rank, target_shape, CLASS_OF( val_a ) );
  GetNArray( val_c, na_c );

  convolve_raw(
    target_rank, na_a->shape, (float*) na_a->ptr,
    target_rank, na_b->shape, (float*) na_b->ptr,
    target_rank, target_shape, (float*) na_c->ptr );

  return val_c;
}

/* @overload nn_run_layer( inputs, weights, thresholds )
 * Calculates activations of a fully-connected neural network layer. The transfer function after
 * summing weights and applying threshold is a "ReLU", equivalent to
 *  y = x < 0.0 ? 0.0 : x
 * this is less sophisticated than many other neural net functions (such as sigma), but is fast to
 * calculate and to train.
 * @param [NArray] inputs must be rank 1 array of floats
 * @param [NArray] weights must be rank 2 array of floats, with first dimension size of inputs, and second dimension size equal to number of outputs
 * @param [NArray] thresholds must be rank 1 array of floats, size equal to number of outputs desired
 * @return [NArray] neuron activations
 */
static VALUE narray_nn_run_single_layer( VALUE self, VALUE inputs, VALUE weights, VALUE thresholds ) {
  struct NARRAY *na_inputs, *na_weights, *na_thresholds, *na_outputs;
  volatile VALUE val_inputs, val_weights, val_thresholds, val_outputs;
  int input_size, output_size;
  int output_shape[1];

  val_inputs = na_cast_object(inputs, NA_SFLOAT);
  GetNArray( val_inputs, na_inputs );
  if ( na_inputs->rank != 1 ) {
    rb_raise( rb_eArgError, "input must be array of rank 1" );
  }
  input_size = na_inputs->total;

  val_weights = na_cast_object(weights, NA_SFLOAT);
  GetNArray( val_weights, na_weights );
  if ( na_weights->rank != 2 ) {
    rb_raise( rb_eArgError, "weights must be array of rank 2" );
  }
  if ( na_weights->shape[0] != input_size ) {
    rb_raise( rb_eArgError, "weights shape mismatch, expected %d across, got %d", input_size, na_weights->shape[0] );
  }
  output_size = na_weights->shape[1];

  val_thresholds = na_cast_object(thresholds, NA_SFLOAT);
  GetNArray( val_thresholds, na_thresholds );
  if ( na_thresholds->rank != 1 ) {
    rb_raise( rb_eArgError, "thresholds must be narray of rank 1" );
  }
  if ( na_thresholds->shape[0] != output_size ) {
    rb_raise( rb_eArgError, "thresholds expected size %d, but got %d", output_size, na_thresholds->shape[0] );
  }

  output_shape[0] = output_size;
  val_outputs = na_make_object( NA_SFLOAT, 1, output_shape, CLASS_OF( val_inputs ) );
  GetNArray( val_outputs, na_outputs );

  nn_run_layer_raw( input_size, output_size, (float*) na_inputs->ptr, (float*) na_weights->ptr,
      (float*) na_thresholds->ptr, (float*) na_outputs->ptr );

  return val_outputs;
}


void Init_convolver() {
  Convolver = rb_define_module( "Convolver" );
  rb_define_singleton_method( Convolver, "convolve", narray_convolve, 2 );
  rb_define_singleton_method( Convolver, "nn_run_layer", narray_nn_run_single_layer, 3 );
  rb_define_singleton_method( Convolver, "fit_kernel_backwards", narray_fit_backwards, 2 );
}