Refactor tensors (matrices).

Tensors now have dynamic length and width.

include/model.h

@@ -4,7 +4,7 @@
 typedef struct _model {
     GLfloat *object;
     GLfloat *colors;
-    GLfloat **transformations;
+    Tensor **transformations;
     size_t bufsize;
     size_t transformation_count;
     size_t transformation_size;

include/tensor.h

@@ -1,11 +1,19 @@
-#ifndef MATRIX_H
+#ifndef TENSOR_H
-#define MATRIX_H
+#define TENSOR_H
 
-float *matrix_new(void);
+typedef struct _tensor {
+    float *data;
+    size_t len;
+    size_t width;
+} Tensor;
 
-float *matrix_multip(float *, float *);
+Tensor *tensor_new(size_t, size_t);
 
-float *matrix_transform(float *, int, float *);
+Tensor *tensor_fromVertexBuffer(float *, size_t);
+
+Tensor *tensor_multip(Tensor *, Tensor *);
+
+void tensor_free(Tensor *);
 
 #endif
 

src/model.c

@@ -6,7 +6,7 @@ model_new(size_t size) {
     self->object = calloc((size ? size : 1) *4 , sizeof(GLfloat));
     self->colors = calloc((size ? size : 1) *3 , sizeof(GLfloat));
     self->bufsize = size;
-    self->transformations = calloc(8 , sizeof(GLfloat*));
+    self->transformations = calloc(8 , sizeof(Tensor *));
     self->transformation_size = 8;
     self->transformation_count = 0;
     return self;
@@ -79,26 +79,33 @@ model_load(const char *path) {
 GLfloat *
 model_applyTransformations(Model *self) {
     // Temporary storage of transformation results
-    GLfloat *temp_buffer[2] = {NULL};
+    Tensor *temp_buffer[2] = {NULL};
-
+    GLfloat *retval;
 
     // BANANA, ROH-TAH-TEH
-    temp_buffer[1] = malloc(self->bufsize * 4 * sizeof(GLfloat));
+    temp_buffer[1] = tensor_fromVertexBuffer(self->object, self->bufsize);
-    memcpy(temp_buffer[1], self->object, self->bufsize * 4 * sizeof(GLfloat));
 
+    // No transformation, create a GLfloat buffer and return the object data.
     if (!self->transformation_count) {
-        return temp_buffer[1];
+        retval = malloc(self->bufsize * 4 * sizeof(GLfloat));
+        memcpy(retval, self->object, self->bufsize * 4 * sizeof(GLfloat));
+        return retval;
     }
 
     int i = 0;
     do {
-        temp_buffer[i%2] = matrix_transform(temp_buffer[(i+1)%2],
+        temp_buffer[i%2] = tensor_multip(self->transformations[i],
-                                          self->bufsize,
+                                         temp_buffer[(i+1)%2]);
-                                          self->transformations[i]);
+        tensor_free(temp_buffer[(i+1)%2]);
-        free(temp_buffer[(i+1)%2]);
     } while (++i < self->transformation_count);
 
-    return temp_buffer[(i+1)%2];
+    retval = malloc(self->bufsize * 4 * sizeof(GLfloat));
+    for (int k = 0; k < self->bufsize; k++) {
+        for (int j = 0; j < 4; j++) {
+            retval[k*4+j] = temp_buffer[(i+1)%2]->data[j*temp_buffer[(i+1)%2]->width+k];
+        }
+    }
+    return retval;
 
 }
 

src/neural.c

@@ -79,9 +79,12 @@ neural_process(Neural_Network *self, float *input) {
         nl = self->layers[i];
         float dot_prod = 0;
         for (int j = 0; j < nl->layer_size; j++) {
+            for (int k = 0; k < nl->layer_size_next; k++) {
+
             // MATH GOES BRRRRRRRR
             dot_prod += nl->neurons[j].value
                         * nl->neurons[j].synapses[j];
+            }
         }
     }
 
@@ -116,14 +119,14 @@ neural_getMesh(ModelRegistry *mr, Neural_Network *nn) {
             }
 
             model = model_circle(0, (GLfloat)1/64);
-            GLfloat *translation_matrix = matrix_new();
+            Tensor *translation_matrix = tensor_new(4, 4);
-            GLfloat *aspectRatio_matrix = matrix_new();
+            Tensor *aspectRatio_matrix = tensor_new(4, 4);
-            aspectRatio_matrix[0] = (GLfloat)9/16;
+            aspectRatio_matrix->data[0] = (GLfloat)9/16;
 
-            translation_matrix[3] = (((GLfloat)-1*16/9)*.90)
+            translation_matrix->data[3] = (((GLfloat)-1*16/9)*.90)
                                     + ((GLfloat)1/(nl->layer_size-1)*2 * i * (((GLfloat)16/9))*.90);
 
-            translation_matrix[7] = .90 - ((GLfloat)1/(nn->layer_count)*2 * j *.90);
+            translation_matrix->data[7] = .90 - ((GLfloat)1/(nn->layer_count)*2 * j *.90);
 
             model->transformations[0] = translation_matrix;
             model->transformations[1] = aspectRatio_matrix;
@@ -132,7 +135,9 @@ neural_getMesh(ModelRegistry *mr, Neural_Network *nn) {
 
             modelRegistry_register(mr, model);
 
+
         }
     }
     return 0;
 }
+

src/tensor.c

@@ -1,62 +1,68 @@
 #include "cx.h"
 
-float *
+Tensor *
-matrix_new() {
+tensor_new(size_t len, size_t width) {
-    float *mat;
+    Tensor *mat;
 
-    mat = calloc(16, sizeof(float));
+    mat = malloc(1 * sizeof(Tensor));
 
-    for (int i = 0; i < 4; i++) {
+    mat->data = calloc(width * len, sizeof(float));
-        mat[i*4+i] = 1;
+    mat->len = len;
+    mat->width = width;
+
+    for (int i = 0; i < len; i++) {
+        mat->data[i*width+(i % width)] = 1;
     }
 
     return mat;
 }
 
-float *
+Tensor *
-matrix_multip(float *mat1, float *mat2) {
+tensor_fromVertexBuffer(float *buffer, size_t bufsize) {
-    float *result;
+    int mat_width;
-    float dot_prod;
+    Tensor *mat;
 
-    result = matrix_new();
+    mat_width = bufsize;
 
-    for (int i = 0; i < 4; i++) {
+    mat = tensor_new(4, mat_width);
     
-        for (int j = 0; j < 4; j++)  {
+    for (int i = 0; i < bufsize; i++) {
-            dot_prod = 0;
-            for (int k = 0; k < 4; k++) {
-                dot_prod += mat1[i*4+k] * mat2[j+k*4];
-            }
-            result[j+i*4] = dot_prod;
-        }
-    }
-    
-    return result;
-}
-
-float *
-matrix_transform(float *vects, int vectcount,
-                 float *mat) {
-    float dot_prod;
-    float *result;
-
-    result = calloc(vectcount*4, sizeof(float));
-
-    for (int k = 0; k < vectcount; k++) {
         for (int j = 0; j < 4; j++) {
-            dot_prod = 0;
+            mat->data[j*mat_width+i] = buffer[i*4+j];
-            for (int i = 0; i < 4; i++) {
-                dot_prod += vects[k*4+i] * mat[i+j*4];
-            }
-            result[j+k*4] = dot_prod;
-        }
-        if (result[k*4+3] != 0.0f) {
-            float div = result[k*4+3];
-            for (int i = 0; i < 4; i++) {
-                result[k*4+i] /= div;
-            }
         }
     }
+    return mat;
+}
+
+Tensor *
+tensor_multip(Tensor *mat2, Tensor *mat1) {
+    Tensor *result;
+    float dot_prod;
+
+    result = tensor_new(mat2->len, mat1->width);
+
+    for (int i = 0; i < mat1->width; i++) {
+
+        for (int j = 0; j < mat2->len; j++)  {
+            dot_prod = 0;
+            for (int k = 0; k < mat1->len; k++) {
+                dot_prod += mat2->data[j*mat2->width+k] * mat1->data[i+(k*mat1->width)];
+            }
+            result->data[i+(j*mat1->width)] = dot_prod;
+        }
+    }
+
+    result->len = mat2->len;
+    result->width = mat1->width;
+
     return result;
 }
 
+void
+tensor_free(Tensor *self) {
+    if (self) {
+        free(self->data);
+    }
+    free(self);
+}
+