diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..20d1c4a
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,8 @@
+*.exe
+*.zip
+*.o
+.vscode
+*.java
+*.classgit 
+weightsdump.txt
+hands/
\ No newline at end of file
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..0675c32
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,8 @@
+CC=gcc
+CFLAGS=-I.
+DEPS = network.c outputFunctions.c errorFunctions.c activationFunctions.c dibdump.c
+
+%.o: %.c $(DEPS)
+	$(CC) -o $@ $^ $(CFLAGS)
+
+makenet: network.o outputFunctions.o errorFunctions.o activationFunctions.o dibdump.o
\ No newline at end of file
diff --git a/README.MD b/README.MD
new file mode 100644
index 0000000..c56681c
--- /dev/null
+++ b/README.MD
@@ -0,0 +1,56 @@
+# Project Info
+This is a set of files that runs a basic neural network. 
+The main file is `network.c`, and the other files 
+provide miscellaneous utilities:  
+   `outputFunctions.c` - stores output functions for use in the network  
+   `activationFunctions.c` - stores activation functions for use in the network  
+   `errorFunctions.c` - stores error functions for use in the network  
+   `dibdump.c` - stores utility functions for use with bitmap i/o  
+  
+Configuration values should be set in a .txt file whose path
+is specified during runtime.
+
+# Running the network
+
+   ```
+   $ gcc -o network network.c outputFunctions.c errorFunctions.c activationFunctions.c dibdump.c
+   $ network.exe
+   ```
+to compile and run the network; enter the path to the config when prompted.
+
+# Config Structure
+
+```
+num_input_nodes            2
+num_hidden_layers          1
+num_output_nodes           3
+
+hidden_layer_1_size        10                   // add lines below this for more hidden layers
+
+trainNetwork               Y                    // whether to train or just run
+print_network_specifics    Y                    // whether to print the specific values of the network
+print_debug_messages       Y                    // whether to print debug messages while running/training
+
+use_bitmap                 Y                    // whether or not to train on a bitmap
+original_bitmap_file       ./input.bmp          // if so, input bitmap file
+output_bitmap_file         ./output.bmp         // if so, output bitmap file
+
+training_sets_file         ./inputs.txt         // file to store input values (bitmap or not)
+where_to_dump_outputs      ./outputs.txt        // where to dump final/periodic output values
+randomize_weights          Y                    // whether to randomize weights
+random_weights_lower       -0.5                 // if randomize: lower bound
+random_weights_upper       0.5                  // if randomize: upper bound
+preset_weights_file        ./weights.txt        // if not randomize: where to read weights from
+where_to_dump_weights      ./weightsdump.txt    // where to dump final/periodic weights 
+dump_every_x_iterations    100                  // dump weights/nodes every _x_ iterations
+
+initial_learning_factor    0.1
+learning_factor_scaler     2.0                  // set this to 1.0 to disable adaptive learning
+min_learning_factor        0.001                // lower and upper bounds
+max_learning_factor        2.0                  // for the learning factor
+enable_weight_rollback     Y                    // whether or not to enable weight rollback
+
+max_training_iterations    100000               // max # of iterations before stopping training
+initial_error              1.0                  // what value to initialize the error at
+target_training_error      0.00001              // target training error (to stop at)
+```
\ No newline at end of file
diff --git a/activationFunctions.c b/activationFunctions.c
new file mode 100644
index 0000000..729489b
--- /dev/null
+++ b/activationFunctions.c
@@ -0,0 +1,22 @@
+/**
+ * Gloria Zhu
+ * Created 9/8/2019
+ * This file holds a collection of activation functions to be used.
+ * 
+ * Functions in this file:
+ * 
+ * identity
+ */
+
+#include <stdlib.h>
+#include <math.h>
+
+#include "./headerfiles/activationFunctions.h"
+
+/**
+ * The identity function just returns the exact input.
+ */
+double identity(double input)
+{
+   return input;
+}
\ No newline at end of file
diff --git a/bitmaps/originalfullsize.bmp b/bitmaps/originalfullsize.bmp
new file mode 100644
index 0000000..f2d7f84
Binary files /dev/null and b/bitmaps/originalfullsize.bmp differ
diff --git a/bitmaps/originalreducedsize.bmp b/bitmaps/originalreducedsize.bmp
new file mode 100644
index 0000000..8a3e725
Binary files /dev/null and b/bitmaps/originalreducedsize.bmp differ
diff --git a/bitmaps/outputreducedsize - 200I 2000A.bmp b/bitmaps/outputreducedsize - 200I 2000A.bmp
new file mode 100644
index 0000000..c6e51f1
Binary files /dev/null and b/bitmaps/outputreducedsize - 200I 2000A.bmp differ
diff --git a/bitmaps/outputreducedsize - 250I 250A.bmp b/bitmaps/outputreducedsize - 250I 250A.bmp
new file mode 100644
index 0000000..778a62c
Binary files /dev/null and b/bitmaps/outputreducedsize - 250I 250A.bmp differ
diff --git a/bitmaps/outputreducedsize - 5197I 500A.bmp b/bitmaps/outputreducedsize - 5197I 500A.bmp
new file mode 100644
index 0000000..861fba4
Binary files /dev/null and b/bitmaps/outputreducedsize - 5197I 500A.bmp differ
diff --git a/bitmaps/outputreducedsize.bmp b/bitmaps/outputreducedsize.bmp
new file mode 100644
index 0000000..c91a2a1
Binary files /dev/null and b/bitmaps/outputreducedsize.bmp differ
diff --git a/configs/andorxorconfig.txt b/configs/andorxorconfig.txt
new file mode 100644
index 0000000..5327f3d
--- /dev/null
+++ b/configs/andorxorconfig.txt
@@ -0,0 +1,32 @@
+num_input_nodes            2
+num_hidden_layers          1
+num_output_nodes           3
+
+hidden_layer_1_size        4
+
+trainNetwork               Y
+print_network_specifics    Y
+print_debug_messages       Y
+
+use_bitmap                 n
+original_bitmap_file       ./originalreducedsize.bmp
+output_bitmap_file         ./outputreducedsize.bmp
+
+training_sets_file         ./inputs/allinputs.txt 
+where_to_dump_outputs      ./inputs/temp.txt
+randomize_weights          Y
+random_weights_lower       -2
+random_weights_upper       2
+preset_weights_file        ./weights/weights.txt
+where_to_dump_weights      ./weights/weightsdump.txt
+dump_every_x_iterations    10000000000
+
+initial_learning_factor    5
+learning_factor_scaler     1.0
+min_learning_factor        0.001
+max_learning_factor        10
+enable_weight_rollback     n
+
+max_training_iterations    100
+initial_error              1.0
+target_training_error      0.00001
\ No newline at end of file
diff --git a/configs/dibdumpconfig.txt b/configs/dibdumpconfig.txt
new file mode 100644
index 0000000..454ab06
--- /dev/null
+++ b/configs/dibdumpconfig.txt
@@ -0,0 +1,33 @@
+num_input_nodes            3136
+num_hidden_layers          2
+num_output_nodes           5
+
+hidden_layer_1_size        100
+hidden_layer_2_size        100
+
+trainNetwork               n
+print_network_specifics    Y
+print_debug_messages       Y
+
+use_bitmap                 n
+original_bitmap_file       ./bitmaps/originalreducedsize.bmp
+output_bitmap_file         ./bitmaps/outputreducedsize.bmp
+
+training_sets_file         ./inputs/bitmapinputs.txt 
+where_to_dump_outputs      ./inputs/bitmapoutput.txt
+randomize_weights          Y
+random_weights_lower       -0.5
+random_weights_upper       0.5
+preset_weights_file        ./weights/weights.txt
+where_to_dump_weights      ./weights/weightsdump.txt
+dump_every_x_iterations    100000
+
+initial_learning_factor    0.1
+learning_factor_scaler     1.0
+min_learning_factor        0.001
+max_learning_factor        5.0
+enable_weight_rollback     n
+
+max_training_iterations    40000
+initial_error              1.0
+target_training_error      0.01
\ No newline at end of file
diff --git a/configs/xorconfig.txt b/configs/xorconfig.txt
new file mode 100644
index 0000000..976f7ed
--- /dev/null
+++ b/configs/xorconfig.txt
@@ -0,0 +1,32 @@
+num_input_nodes            2
+num_hidden_layers          1
+num_output_nodes           1
+
+hidden_layer_1_size        1
+
+trainNetwork               Y
+print_network_specifics    Y
+print_debug_messages       n
+
+use_bitmap                 n
+original_bitmap_file       ./originalreducedsize.bmp
+output_bitmap_file         ./outputreducedsize.bmp
+
+training_sets_file         ./inputs/xorinputs.txt 
+where_to_dump_outputs      ./inputs/temp.txt
+randomize_weights          Y
+random_weights_lower       -2
+random_weights_upper       2
+preset_weights_file        ./weights/weights.txt
+where_to_dump_weights      ./weights/weightsdump.txt
+dump_every_x_iterations    10
+
+initial_learning_factor    5
+learning_factor_scaler     1.0
+min_learning_factor        0.001
+max_learning_factor        10
+enable_weight_rollback     Y
+
+max_training_iterations    10000
+initial_error              1.0
+target_training_error      0.00001
\ No newline at end of file
diff --git a/dibdump.c b/dibdump.c
new file mode 100644
index 0000000..c94e1d7
--- /dev/null
+++ b/dibdump.c
@@ -0,0 +1,262 @@
+/**
+ * Gloria Zhu / Adapted from Dr. Nelson
+ * Created 11/23/2019
+ * This file is responsible for reading in 24-bit and 32-bit bitmaps
+ * and converting them to activation values. It is also responsible 
+ * for reconstructing bitmaps from a text file of values.
+ * 
+ * Functions in this file:
+ * int* readBitmap(char* inFileName, char* pelsOutputFile)
+ * void writePelsToTextFile(unsigned int *pels, int numPels, char *pelsOutputFile)
+ * void writeBitmap(char *pelsOutputFile, char *originalDIBFile, char *outputDIBFile)
+ * void writeBitmapHelper(char *outFileName, unsigned int *pels, int numPels, BITMAPFILEHEADER bmpFileHeader, BITMAPINFOHEADER bmpInfoHeader)
+{
+ */
+
+#include <windows.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <errno.h>
+
+#include "./headerfiles/dibdump.h"
+
+/**
+ * This function reads in a bitmap and writes out its pels to a text file.
+ * Note: this will only run for 24-bit and 32-bit bitmaps (that don't have a color table)
+ * 
+ * @param inFileName the name of the input bitmap file
+ * @param pelsOutputFile the file to output the pel values to
+ * @return a pointer to the array of pels (in blue|green|red|reserved byte form)
+ */
+int *readBitmap(char *inFileName, char *pelsOutputFile)
+{
+   BITMAPFILEHEADER bmpFileHeader;
+   BITMAPINFOHEADER bmpInfoHeader;
+
+   char topDownDIB = 'n';
+
+   unsigned int *pels; // using unsigned int to prevent overflow errors
+
+   FILE *inFile = fopen(inFileName, "rb");
+
+   if (inFile != NULL)
+   {
+      fread((char *)&bmpFileHeader, sizeof(BITMAPFILEHEADER), 1, inFile);
+
+      printf("bfType=%2X\nbfSize=%lu\nbfReserved1=%u\nbfReserved2=%u\nbfOffBits=%lu\n",
+             bmpFileHeader.bfType,
+             bmpFileHeader.bfSize,
+             bmpFileHeader.bfReserved1,
+             bmpFileHeader.bfReserved2,
+             bmpFileHeader.bfOffBits);
+
+      fread((char *)&bmpInfoHeader, sizeof(BITMAPINFOHEADER), 1, inFile);
+
+      printf("biSize=%lu\nbiWidth=%ld\nbiHeight=%ld\nbiPlanes=%u\nbiBitCount=%u\nbiCompression=%lu\nbiSizeImage=%lu\nbiXPelsPerMeter=%ld\nbiYPelsPerMeter=%ld\nbiClrUsed=%lu\nbiClrImportant=%lu\n\n",
+             bmpInfoHeader.biSize,
+             bmpInfoHeader.biWidth,
+             bmpInfoHeader.biHeight,
+             bmpInfoHeader.biPlanes,
+             bmpInfoHeader.biBitCount,
+             bmpInfoHeader.biCompression,
+             bmpInfoHeader.biSizeImage,
+             bmpInfoHeader.biXPelsPerMeter,
+             bmpInfoHeader.biYPelsPerMeter,
+             bmpInfoHeader.biClrUsed,
+             bmpInfoHeader.biClrImportant);
+
+      int biHeightAbs = bmpInfoHeader.biHeight;
+
+      if (bmpInfoHeader.biHeight < 0)
+      {
+         topDownDIB = 'Y';
+         biHeightAbs = -biHeightAbs;
+      }
+
+      // allocating space for the pels
+      pels = malloc(bmpInfoHeader.biWidth * biHeightAbs * sizeof(int));
+
+      int bytesPerPixel = bmpInfoHeader.biBitCount / 8;
+
+      if (bytesPerPixel == 4) // 32-bit bitmap
+      {
+         fread(pels, bytesPerPixel, bmpInfoHeader.biWidth * biHeightAbs, inFile);
+
+         for (int i = 0; i < bmpInfoHeader.biWidth * biHeightAbs; i++)
+         {
+            unsigned int pel = pels[i];
+
+            // pels are read in reserved|red|green|blue order
+            int blue = pel & 0x00FF;
+            int green = (pel >> 8) & 0x00FF;
+            int red = (pel >> 16) & 0x00FF;
+            int reserved = (pel >> 24) & 0x00FF;
+            // rearranging pels into blue|green|red|reserved order
+            unsigned int newpel = blue << 24 | green << 16 | red << 8 | reserved;
+
+            pels[i] = newpel;
+         }
+
+         writePelsToTextFile(pels, bmpInfoHeader.biWidth * biHeightAbs, pelsOutputFile);
+      }
+      else if (bytesPerPixel == 3) // 24-bit bitmap
+      {
+         fread(pels, bytesPerPixel, bmpInfoHeader.biWidth * biHeightAbs, inFile);
+
+         writePelsToTextFile(pels, bmpInfoHeader.biWidth * biHeightAbs, pelsOutputFile);
+      }
+      else // if the bitmap is not 24 or 32 bit (bytesPerPixel is not 3 or 4)
+      {
+         fprintf(stderr, "ERROR: Bitmap must be 24-bit or 32-bit.");
+      }
+   }
+   else // if inFile == NULL
+   {
+      fprintf(stderr, "INPUT ERROR: %s\n", strerror(errno));
+   }
+
+   return pels;
+}
+
+/**
+ * This function writes an array of pels to a text file in hexadecimal form.
+ * It concatenates a 1 to the front and two copies of the pels to fit
+ * the input txt file format of the network.
+ * 
+ * @param pels array of the pel values
+ * @param numPels the total number of pels
+ * @param pelsOutputFile the file to write the pels to
+ */
+void writePelsToTextFile(unsigned int *pels, int numPels, char *pelsOutputFile)
+{
+   printf("Writing pels to text file...\n");
+   FILE *outFile = fopen(pelsOutputFile, "a+b");
+
+   // fprintf(outFile, "1\n"); uncomment me for original code submission
+
+   for (int i = 0; i < numPels; i++)
+   {
+      fprintf(outFile, "%x\n", pels[i]);
+   }
+
+   for (int i = 0; i < numPels; i++)
+   {
+      fprintf(outFile, "%x\n", pels[i]);
+   }
+
+   fclose(outFile);
+
+   printf("Finished writing %d pels to text file %s\n", numPels, pelsOutputFile);
+
+   return;
+}
+
+/**
+ * This function transfers from pel .txt file to a bitmap, using
+ * the original bitmap file as a reference. It is responsible for
+ * reading in the file/info headers of the original DIB file, and
+ * then passing those values + the pels to a helper function.
+ * 
+ * @param pelsOutputFile the file to read pels in from
+ * @param originalDIBFile the original bitmap file to reference for header/info values
+ * @param outputDIBFile the bitmap file to output to
+ */
+void writeBitmap(char *pelsOutputFile, char *originalDIBFile, char *outputDIBFile)
+{
+   FILE *origFile = fopen(originalDIBFile, "rb");
+   FILE *pelsFile = fopen(pelsOutputFile, "r");
+
+   unsigned int *pels;
+
+   if (origFile != NULL)
+   {
+      BITMAPFILEHEADER bmpFileHeader;
+      BITMAPINFOHEADER bmpInfoHeader;
+
+      fread((char *)&bmpFileHeader, sizeof(BITMAPFILEHEADER), 1, origFile);
+      fread((char *)&bmpInfoHeader, sizeof(BITMAPINFOHEADER), 1, origFile);
+
+      int biHeightAbs = bmpInfoHeader.biHeight;
+      char topDownDIB = 'n';
+
+      if (bmpInfoHeader.biHeight < 0)
+      {
+         topDownDIB = 'Y';
+         biHeightAbs = -biHeightAbs;
+      }
+
+      pels = malloc(bmpInfoHeader.biWidth * biHeightAbs * sizeof(int));
+
+      unsigned int pel;
+
+      for (int i = 0; i < bmpInfoHeader.biWidth * biHeightAbs; i++)
+      {
+         fscanf(pelsFile, "%x", &pel);
+         pels[i] = pel;
+      }
+
+      writeBitmapHelper(outputDIBFile, pels, bmpInfoHeader.biWidth * biHeightAbs, bmpFileHeader, bmpInfoHeader);
+
+      fclose(origFile);
+      fclose(pelsFile);
+
+      printf("Finished writing pels from %s to bitmap file %s based on original bitmap %s\n",
+             pelsOutputFile,
+             outputDIBFile,
+             originalDIBFile);
+
+   } // if (origFile == NULL)
+
+   return;
+}
+
+/**
+ * This function transfers from pel .txt file to a bitmap, having been
+ * given the file/info headers of the original bitmap file.
+ * 
+ * @param outFileName the name of the output dibdump file
+ * @param pels the array of pels
+ * @param numPels the total number of pels
+ * @param bmpFileHeader the bitmap file header to use
+ * @param bmpInfoHeader the bitmap info header to use
+ */
+void writeBitmapHelper(
+    char *outFileName,
+    unsigned int *pels,
+    int numPels,
+    BITMAPFILEHEADER bmpFileHeader,
+    BITMAPINFOHEADER bmpInfoHeader)
+{
+
+   FILE *outFile = fopen(outFileName, "wb");
+
+   unsigned int pel;
+   for (int i = 0; i < numPels; i++)
+   {
+      pel = pels[i];
+      int reserved = pel & 0x00FF;
+      int red = (pel >> 8) & 0x00FF;
+      int green = (pel >> 16) & 0x00FF;
+      int blue = (pel >> 24) & 0x00FF;
+      // rearranging the pels back into their proper structure
+      unsigned int newpel = reserved << 24 | red << 16 | green << 8 | blue;
+
+      pels[i] = newpel;
+   }
+
+   if (outFile != NULL)
+   {
+      fwrite(&bmpFileHeader, sizeof(BITMAPFILEHEADER), 1, outFile);
+      fwrite(&bmpInfoHeader, sizeof(BITMAPINFOHEADER), 1, outFile);
+      fwrite(pels, 4, numPels, outFile);
+      fclose(outFile);
+
+      printf("Finished writing %d pels to bitmap output %s\n", numPels, outFileName);
+   }
+   else
+   {
+      fprintf(stderr, "OUTPUT ERROR: %s\n", strerror(errno));
+   }
+
+   return;
+}
diff --git a/errorFunctions.c b/errorFunctions.c
new file mode 100644
index 0000000..12523a4
--- /dev/null
+++ b/errorFunctions.c
@@ -0,0 +1,39 @@
+/**
+ * Gloria Zhu
+ * Created 9/8/2019
+ * This file holds a collection of error functions to be used
+ * for evaluating error in a neural network. Note that every 
+ * function here should take in as parameters two arrays of
+ * float values of the same length, the first being the
+ * expected output and the second being the actual output,
+ * as well as an integer that is the length of the two arrays.
+ * 
+ * Functions in this file:
+ * 
+ * quadraticLoss
+ */
+
+#include <stdlib.h>
+#include <math.h>
+
+#include "./headerfiles/errorFunctions.h"
+
+/**
+ * The quadratic loss error function returns half the sum of the squares of the
+ * differences between expected outputs and actual outputs.
+ * 
+ * @param expectedOutput array of the expected outputs
+ * @param actualOutput array of the actual outputs
+ * @param arrayLength the number of outputs to compare and use to calculate error
+ */
+double quadraticLoss(double expectedOutput[], double actualOutput[], int arrayLength)
+{
+   double error = 0.0;
+   for (int i = 0; i < arrayLength; i++)
+   {
+      double deviation = expectedOutput[0] - actualOutput[0];
+      error += deviation * deviation;
+   }
+   return 0.5 * error;
+}
+
diff --git a/handprocessor.c b/handprocessor.c
new file mode 100644
index 0000000..8e67465
--- /dev/null
+++ b/handprocessor.c
@@ -0,0 +1,32 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+
+#include "./headerfiles/outputFunctions.h"     // importing output,
+#include "./headerfiles/activationFunctions.h" // activation, and
+#include "./headerfiles/errorFunctions.h"      // error functions
+
+#include "./headerfiles/dibdump.h" // importing dibdump functions
+
+#define MAX_FILE_NAME_LENGTH 2048        // max characters in a file name
+
+#define NUM_BITMAPS_TO_PROCESS 5
+
+int main()
+{
+   char * pelsOutputFile = "./inputs/bitmapinputs.txt";
+
+   FILE *outFile = fopen(pelsOutputFile, "a");
+   fprintf(outFile, "5\n");
+
+   readBitmap("./hands/Gloria1.bmp", pelsOutputFile);
+   fprintf(outFile, "1\n0\n0\n0\n0\n");
+   readBitmap("./hands/Gloria2.bmp", pelsOutputFile);
+   fprintf(outFile, "0\n1\n0\n0\n0\n");
+   readBitmap("./hands/Gloria3.bmp", pelsOutputFile);
+   fprintf(outFile, "0\n0\n1\n0\n0\n");
+   readBitmap("./hands/Gloria4.bmp", pelsOutputFile);
+   fprintf(outFile, "0\n0\n0\n1\n0\n");
+   readBitmap("./hands/Gloria5.bmp", pelsOutputFile);
+   fprintf(outFile, "0\n0\n0\n0\n1\n");
+}
\ No newline at end of file
diff --git a/headerfiles/activationFunctions.h b/headerfiles/activationFunctions.h
new file mode 100644
index 0000000..1bf5632
--- /dev/null
+++ b/headerfiles/activationFunctions.h
@@ -0,0 +1,13 @@
+/**
+ * Gloria Zhu
+ * Created 9/15/2019
+ * This file contains the header files for activation functions. 
+ * More specific documentation can be found in the source file.
+ */ 
+
+#ifndef activationFunctions_h
+#define activationFunctions_h
+
+double identity(double);
+
+#endif
\ No newline at end of file
diff --git a/headerfiles/dibdump.h b/headerfiles/dibdump.h
new file mode 100644
index 0000000..2c0ff58
--- /dev/null
+++ b/headerfiles/dibdump.h
@@ -0,0 +1,18 @@
+/**
+ * Gloria Zhu
+ * Created 11/15/2019
+ * This file contains the header files for dibdump-related functions. 
+ * More specific documentation can be found in the source file.
+ */
+
+#ifndef dibdump_h
+#define dibdump_h
+
+#include <windows.h>
+
+int *readBitmap(char *, char *);
+void writePelsToTextFile(unsigned int *, int, char *);
+void writeBitmap(char *, char *, char *);
+void writeBitmapHelper(char *, unsigned int *, int, BITMAPFILEHEADER, BITMAPINFOHEADER);
+
+#endif
\ No newline at end of file
diff --git a/headerfiles/errorFunctions.h b/headerfiles/errorFunctions.h
new file mode 100644
index 0000000..2cdee1f
--- /dev/null
+++ b/headerfiles/errorFunctions.h
@@ -0,0 +1,13 @@
+/**
+ * Gloria Zhu
+ * Created 9/15/2019
+ * This file contains the header files for error functions. 
+ * More specific documentation can be found in the source file.
+ */
+
+#ifndef errorFunctions_h
+#define errorFunctions_h
+
+double quadraticLoss(double[], double[], int);
+
+#endif
\ No newline at end of file
diff --git a/headerfiles/outputFunctions.h b/headerfiles/outputFunctions.h
new file mode 100644
index 0000000..87eac52
--- /dev/null
+++ b/headerfiles/outputFunctions.h
@@ -0,0 +1,20 @@
+/**
+ * Gloria Zhu
+ * Created 9/15/2019
+ * This file contains the header files for output functions. 
+ * More specific documentation can be found in the source file.
+ */
+
+#ifndef outputFunctions_h
+#define outputFunctions_h
+
+double sigmoid(double);
+double sigmoidDeriv(double);
+
+double tanh(double);
+double tanhDeriv(double);
+
+double relu(double);
+double reluDeriv(double);
+
+#endif
\ No newline at end of file
diff --git a/inputs/allinputs.txt b/inputs/allinputs.txt
new file mode 100644
index 0000000..97f266a
--- /dev/null
+++ b/inputs/allinputs.txt
@@ -0,0 +1,21 @@
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\ No newline at end of file
diff --git a/inputs/andinputs.txt b/inputs/andinputs.txt
new file mode 100644
index 0000000..5606db2
--- /dev/null
+++ b/inputs/andinputs.txt
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\ No newline at end of file
diff --git a/inputs/bitmapinputs.txt b/inputs/bitmapinputs.txt
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index 0000000..5e5b919
--- /dev/null
+++ b/inputs/bitmapinputs.txt
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diff --git a/inputs/orinputs.txt b/inputs/orinputs.txt
new file mode 100644
index 0000000..2f58fcd
--- /dev/null
+++ b/inputs/orinputs.txt
@@ -0,0 +1,13 @@
+4
+0
+0
+0
+1
+0
+1
+0
+1
+1
+1
+1
+1
\ No newline at end of file
diff --git a/inputs/temp.txt b/inputs/temp.txt
new file mode 100644
index 0000000..5bc24a9
--- /dev/null
+++ b/inputs/temp.txt
@@ -0,0 +1 @@
+7fffffff
diff --git a/inputs/xorinputs.txt b/inputs/xorinputs.txt
new file mode 100644
index 0000000..1a2e301
--- /dev/null
+++ b/inputs/xorinputs.txt
@@ -0,0 +1,13 @@
+4
+0
+0
+0
+0
+1
+1
+1
+0
+1
+1
+1
+0
\ No newline at end of file
diff --git a/network.c b/network.c
new file mode 100644
index 0000000..278aaad
--- /dev/null
+++ b/network.c
@@ -0,0 +1,843 @@
+/**
+ * Gloria Zhu
+ * Created 9/7/2019
+ * This file defines and runs a network.
+ * 
+ * Functions in this file:
+ * 
+ * void parseConfig(void)
+ * void takeDimensionInputs(void)
+ * void takeTrainingSetsInputs(void)
+ * void initializeWeightsFromFile(void)
+ * void initializeWeightsRandomly(double, double)
+ * double randomNumber(double, double)
+ * void writeWeightsToFile(void)
+ * void writeOutputsToFile(void)
+ * void calculateNumNodesAndWeights(void)
+ * void freeMemory(void)
+ * 
+ * void printWeights(void)
+ * void printNetworkConfiguration(void)
+ * void runNetwork(void)
+ * 
+ * double calculateError(void)
+ * void runForAllTrainingSets(void);
+ * void trainForAllTrainingSets(void);
+ * void train(int, double);
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <time.h> // need this library to get unique seed (current unix time) for rng
+
+#include "./headerfiles/outputFunctions.h"     // importing output,
+#include "./headerfiles/activationFunctions.h" // activation, and
+#include "./headerfiles/errorFunctions.h"      // error functions
+
+#include "./headerfiles/dibdump.h" // importing dibdump functions
+
+#define MAX_FILE_NAME_LENGTH 2048        // max characters in a file name
+#define UNSIGNED_INT_SCALER 4294967295.0 // used for scaling the pels to [0,1]
+
+/**
+ * This function pointer refers to the output function
+ * to be used in determining a node's output according
+ * to its activation level. Output functions are defined
+ * in ./outputFunctions.c and included in this file.
+ */
+double (*outputFunction)(double value) = &sigmoid; // set the output function here
+
+/**
+ * This function pointer refers to the output derivative function
+ * to be used in determining partial derivatives. They are defined
+ * in ./outputFunctions.c and included in this file.
+ */
+double (*outputDerivFunction)(double value) = &sigmoidDeriv; // set the output derivative function here
+
+/**
+ * This function pointer refers to the activation function
+ * to be used in calculating the activation of a unit according 
+ * to input. Activation functions are 
+ * defined in ./activationFunctions.c and included in this file.
+ */
+double (*activationFunction)(double input) = &identity; // set the activation function here
+
+/**
+ * This function pointer refers to the error function
+ * to be used in calculating error. Error functions are
+ * defined in ./errorFunctions.c and included in this file.
+ */
+double (*errorFunction)(
+    double expectedOutput[],
+    double actualOutput[],
+    int numNodes) = &quadraticLoss; // set the error function here
+
+// function headers ----------------------
+
+// functions that handle utility tasks like i/o and mem allocation
+void parseConfig(void);
+void takeDimensionInputs(void);
+void takeTrainingSetsInputs(void);
+void initializeWeightsFromFile(void);
+void initializeWeightsRandomly(double, double);
+double randomNumber(double, double);
+void writeWeightsToFile(void);
+void writeOutputsToFile(void);
+void calculateNumNodesAndWeights(void);
+void freeMemory(void);
+
+// functions for printing and debugging
+void printWeights(void);
+void printNetworkConfiguration(void);
+
+// functions that run/train the network
+void runNetwork(void);
+double calculateError(void);
+void runForAllTrainingSets(void);   // does not train
+void trainForAllTrainingSets(void); // helper function
+void train(int, double);
+
+// variable declarations ----------------------
+
+// variables that describe the structure of the network
+char configFilename[MAX_FILE_NAME_LENGTH];
+
+int numLayers;
+int numHiddenLayers;
+int numInputNodes;
+int *layerDimensions;
+int numOutputNodes;
+
+char useRandomWeights;
+
+// arrays that hold the actual values of the network
+double *nodes;
+double *weights;
+double *expectedOutputs;
+
+// backprop arrays
+double *thetas;
+double *psis;
+
+// calculated values related to the structure of the network
+int totalWeights;
+int maxNodesInALayer;
+int maxWeightsInALayer;
+
+// file paths for i/o files
+char weightsFileInput[MAX_FILE_NAME_LENGTH];
+char weightsFileOutput[MAX_FILE_NAME_LENGTH];
+
+char nodesFileInput[MAX_FILE_NAME_LENGTH];
+char nodesFileOutput[MAX_FILE_NAME_LENGTH];
+int numTrainingSets;
+
+char useBitmap;
+char bitmapFileInput[MAX_FILE_NAME_LENGTH];
+char bitmapFileOutput[MAX_FILE_NAME_LENGTH];
+
+// values related to training
+char trainNetwork;          // whether or not to train (Y for yes, anything else for no)
+char printNetworkSpecifics; // whether or not to print the specific values of the network
+char printDebugMessages;    // whether or not to print debug messages
+char enableWeightRollback;  // whether or not to enable weight rollback
+
+double *trainingSets; // stores training set values
+
+double error;                // current error of network (set to some initial config value)
+double learningFactor;       // current lambda value
+double learningFactorScaler; // lambda scaler
+
+double minLearningFactor; // lower bound for lambda value
+double maxLearningFactor; // upper bound for lambda value
+
+int dumpEveryIterations; // dump weights/outputs every _x_ iterations
+
+int maxIterations;  // max number of iterations before stopping
+double targetError; // training stops when error reaches this value
+
+/**
+ * The main function makes the actual calls that complete parts
+ * of the process of running a neural network.
+ */
+int main()
+{
+    printf("What config file should I use? ");
+    scanf("%s", &configFilename);
+    parseConfig();
+
+    printf("\nINITIAL NETWORK:\n");
+    runForAllTrainingSets();
+
+    clock_t CPU_time_1 = clock();
+
+    if (trainNetwork == 'Y')
+    {
+        printf("AFTER TRAINING:\n");
+        train(maxIterations, targetError);
+   }
+
+   clock_t CPU_time_2 = clock();
+
+   writeWeightsToFile();
+   writeOutputsToFile();
+
+   if (useBitmap == 'Y')
+   {
+      writeBitmap(nodesFileOutput, bitmapFileInput, bitmapFileOutput);
+   }
+
+   freeMemory();
+
+   printf("Time taken: %fms", ((double)(CPU_time_2 - CPU_time_1)) / CLOCKS_PER_SEC * 1000);
+
+   return 0;
+}
+
+/**
+ * This function parses in all of the network's options through the
+ * config file (see the README for details on the options).
+ */
+void parseConfig()
+{
+   FILE *config = fopen(configFilename, "r");
+
+   char dummy[MAX_FILE_NAME_LENGTH]; // dummy value for handling the descriptor strings in the config file
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%d", &numInputNodes); // reading in number of input nodes
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%d", &numHiddenLayers); // reading in number of hidden layers
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%d", &numOutputNodes); // reading in number of output nodes
+
+   numLayers = numHiddenLayers + 2; // setting some network structure values
+   layerDimensions = calloc(numLayers, sizeof(int));
+   layerDimensions[0] = numInputNodes;
+   layerDimensions[numLayers - 1] = numOutputNodes;
+
+   for (int i = 0; i < numHiddenLayers; i++) // taking in hidden layer dimensions
+   {
+      fscanf(config, "%s", &dummy);
+      fscanf(config, "%d", layerDimensions + i + 1);
+   }
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%s", &trainNetwork); // whether to train or just run instead
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%s", &printNetworkSpecifics); // whether or not to print network specifics
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%s", &printDebugMessages); // whether or not to print debug messages
+
+   calculateNumNodesAndWeights(); // calculating some useful values
+
+   nodes = malloc(maxNodesInALayer * numLayers * sizeof(double)); // allocating memory
+   if (nodes == NULL)
+   {
+      printf("There was an error allocating memory for nodes.\n");
+   }
+   weights = malloc(maxWeightsInALayer * numLayers * sizeof(double));
+   if (weights == NULL)
+   {
+      printf("There was an error allocating memory for weights.\n");
+   }
+   expectedOutputs = malloc(numOutputNodes * sizeof(double));
+   if (expectedOutputs == NULL)
+   {
+      printf("There was an error allocating memory for expected outputs.\n");
+   }
+
+   thetas = malloc(maxNodesInALayer * numLayers * sizeof(double));
+   if (thetas == NULL)
+   {
+      printf("There was an error allocating memory for thetas.\n");
+   }
+   psis = malloc(maxNodesInALayer * numLayers * sizeof(double));
+   if (psis == NULL)
+   {
+      printf("There was an error allocating memory for psis.\n");
+   }
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%s", &useBitmap); // whether or not to use bitmaps
+   printf("use bitmap? %c\n", useBitmap);
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%s", &bitmapFileInput); // input bitmap file
+   printf("bitmap input: %s\n", bitmapFileInput);
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%s", &bitmapFileOutput); // outputted bitmap file
+   printf("bitmap output: %s\n", bitmapFileOutput);
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%s", nodesFileInput); // reading in training sets
+   printf("nodes input: %s\n", nodesFileInput);
+
+   if (useBitmap == 'Y')
+   {
+      readBitmap(bitmapFileInput, nodesFileInput);
+   }
+
+   takeTrainingSetsInputs();
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%s", nodesFileOutput); // where it would dump output values
+   printf("nodes output: %s\n", nodesFileOutput);
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%s", &useRandomWeights); // whether or not to randomize weights
+   printf("use random weights? %c\n", useRandomWeights);
+
+   double randomWeightsLowerBound;
+   double randomWeightsUpperBound;
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%lf", &randomWeightsLowerBound); // reading in randomized weights' lower bound
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%lf", &randomWeightsUpperBound); // reading in randomized weights' lower bound
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%s", &weightsFileInput); // where it would read preset weights from
+   printf("weights input: %s\n", weightsFileInput);
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%s", &weightsFileOutput); // where it would dump weights to
+   printf("weights output: %s\n", weightsFileOutput);
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%d", &dumpEveryIterations); // where it would dump weights to
+
+   if (useRandomWeights == 'Y')
+   {
+      initializeWeightsRandomly(randomWeightsLowerBound, randomWeightsUpperBound);
+   }
+   else
+   {
+      initializeWeightsFromFile();
+   }
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%lf", &learningFactor); // reading in initial learning factor
+   printf("learning factor: %lf\n", learningFactor);
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%lf", &learningFactorScaler); // reading in learning factor scaler
+   printf("learning factor scaler: %lf\n", learningFactorScaler);
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%lf", &minLearningFactor); // reading in minimum allowed learning factor
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%lf", &maxLearningFactor); // reading in maximum allowed learning factor
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%s", &enableWeightRollback); // whether or not to enable weight rollback
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%d", &maxIterations); // reading in max iterations for training
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%lf", &error); // reading in initial error
+
+   fscanf(config, "%s", &dummy);
+   fscanf(config, "%lf", &targetError); // reading in target error
+
+   fclose(config);
+}
+
+/**
+ * This function allocates space for all the training sets 
+ * according to the number of training sets (first line of
+ * the input file) and the number of input and output nodes 
+ * (set in the config file). 
+ * It then reads in the values and stores them.
+ */
+void takeTrainingSetsInputs()
+{
+   if (useBitmap == 'Y') // take input from a bitmap
+   {
+      unsigned int node = 0.0;
+      FILE *nodesFile = fopen(nodesFileInput, "r");
+
+      fscanf(nodesFile, "%x", &numTrainingSets);
+
+      printf("num training sets: %d\n", numTrainingSets);
+
+      trainingSets = calloc(numTrainingSets * (numInputNodes + numOutputNodes), sizeof(double));
+
+      for (int i = 0; i < numTrainingSets * (numInputNodes + numOutputNodes); i++)
+      {
+         fscanf(nodesFile, "%x", &node);
+         // printf("%u divided by %lf is %lf\n", (unsigned int) node, UNSIGNED_INT_SCALER, ((double)node) / UNSIGNED_INT_SCALER);
+         *(trainingSets + i) = ((double)node) / UNSIGNED_INT_SCALER;
+      }
+
+      fclose(nodesFile);
+   }
+   else // take input from a pre-setup file
+   {
+      double node = 0.0;
+      FILE *nodesFile = fopen(nodesFileInput, "r");
+
+      fscanf(nodesFile, "%x", &numTrainingSets);
+
+      trainingSets = calloc(numTrainingSets * (numInputNodes + numOutputNodes), sizeof(double));
+
+      for (int i = 0; i < numTrainingSets * (numInputNodes + numOutputNodes); i++)
+      {
+         fscanf(nodesFile, "%lf", &node);
+         *(trainingSets + i) = node;
+      }
+
+      fclose(nodesFile);
+   }
+
+   return;
+}
+
+/**
+ * This function initializes the weights to known values from
+ * a file. Weights are stored in mkj order.
+ */
+void initializeWeightsFromFile()
+{
+   double weight = 0.0;
+   FILE *weightsFile = fopen(weightsFileInput, "r");
+
+   for (int i = 0; i < totalWeights; i++)
+   {
+      fscanf(weightsFile, "%lf", &weight);
+      *(weights + i) = weight;
+   }
+
+   fclose(weightsFile);
+
+   return;
+}
+
+/**
+ * Initializes all weights randomly to values between given bounds.
+ * Randomization uses the current time as its seed.
+ * 
+ * @param lowerBound the lower bound of the randomized weights
+ * @param upperBound the upper bound of the randomized weights
+ */
+void initializeWeightsRandomly(double lowerBound, double upperBound)
+{
+   srand(time(0));
+   for (int m = 0; m < numLayers - 1; m++)
+   {
+      for (int j = 0; j < layerDimensions[m]; j++)
+      {
+         for (int k = 0; k < layerDimensions[m + 1]; k++)
+         {
+            unsigned int index = m * maxWeightsInALayer + j * maxNodesInALayer + k;
+            double randWeight = randomNumber(lowerBound, upperBound);
+
+            weights[index] = randWeight;
+         }
+      }
+   }
+   printf("Finished initializing weights\n");
+   return;
+}
+
+/**
+ * @return a random number between a given lower and upper bound
+ * 
+ * @param lowerBound the lower bound of the randomized weights
+ * @param upperBound the upper bound of the randomized weights
+ */ 
+double randomNumber(double lowerBound, double upperBound) {
+   return (double)rand() / (double)RAND_MAX * (upperBound - lowerBound) + lowerBound;
+}
+
+/**
+ * This function write the current weights to a file.
+ * Weights are stored in mkj order.
+ */
+void writeWeightsToFile()
+{
+   FILE *weightsFile = fopen(weightsFileOutput, "w+");
+
+   for (int i = 0; i < totalWeights; i++)
+   {
+      fprintf(weightsFile, "%lf\n", weights[i]);
+   }
+
+   fclose(weightsFile);
+
+   return;
+}
+
+/**
+ * This function writes all the current outputs to a specified file.
+ */
+void writeOutputsToFile()
+{
+   FILE *outFile = fopen(nodesFileOutput, "w");
+
+   for (int i = 0; i < numOutputNodes; i++)
+   {
+      fprintf(outFile, "%x\n", (unsigned int)(nodes[maxNodesInALayer * (numLayers - 1) + i] * UNSIGNED_INT_SCALER));
+   }
+
+   fclose(outFile);
+
+   return;
+}
+
+/**
+ * This function is responsible for calculating the maximum nodes
+ * and maximum weights in a layer. These values are used for
+ * allocating space.
+ */
+void calculateNumNodesAndWeights()
+{
+   for (int layer = 0; layer < numLayers; layer++)
+   {
+      // maintaining maxNodesInALayer
+      if (layerDimensions[layer] > maxNodesInALayer)
+      {
+         maxNodesInALayer = layerDimensions[layer];
+      }
+   }
+
+   maxWeightsInALayer = maxNodesInALayer * maxNodesInALayer;
+   totalWeights = maxWeightsInALayer * (numLayers - 1);
+
+   return;
+}
+
+/**
+ * This function actually runs the network (which is assumed
+ * to have already been initialized with inputs and weights).
+ * It does not do any error calculation or training and merely
+ * propagates values throughout the nodes, while collecting
+ * theta values to be used in backprop.
+ */
+void runNetwork()
+{
+   for (int m = 0; m < numLayers - 1; m++) // looping through connectivity layers
+   {
+      int numSourceNodes = layerDimensions[m];
+      int numDestNodes = layerDimensions[m + 1];
+
+      for (int j = 0; j < numDestNodes; j++) // looping through right layer
+      {
+         int destNodeIndex = (m + 1) * maxNodesInALayer + j;
+         thetas[destNodeIndex] = 0.0;
+
+         for (int k = 0; k < numSourceNodes; k++) // looping through left layer
+         {
+            int sourceNodeIndex = m * maxNodesInALayer + k;
+            int currentWeightIndex = m * maxNodesInALayer * maxNodesInALayer + k * maxNodesInALayer + j;
+
+            thetas[destNodeIndex] += activationFunction(weights[currentWeightIndex] * nodes[sourceNodeIndex]);
+         } // for (int k = 0; k < numSourceNodes; k++)
+
+         nodes[destNodeIndex] = outputFunction(thetas[destNodeIndex]);
+      } // for (int j = 0; j < numDestNodes; j++)
+   }    // for (int m = 0; m < numLayers - 1; m++)
+
+   return;
+}
+
+/**
+ * This function calculates the error of a network (that
+ * should already have been run) according to the
+ * error function defined above.
+ */
+double calculateError()
+{
+   double *actualOutputs = calloc(numOutputNodes, sizeof(double));
+
+   // storing actual outputs into an array to pass into the error function
+   int nodeIndex = (numLayers - 1) * maxNodesInALayer;
+   for (int i = 0; i < numOutputNodes; i++)
+   {
+      actualOutputs[i] = nodes[nodeIndex + i];
+   }
+
+   double error = errorFunction(expectedOutputs, actualOutputs, numOutputNodes);
+
+   free(actualOutputs);
+
+   return error;
+}
+
+/**
+ * This function is responsible for freeing memory after running/training the network.
+ */
+void freeMemory()
+{
+   free(layerDimensions);
+   free(nodes);
+   free(weights);
+   free(expectedOutputs);
+   free(thetas);
+   free(psis);
+
+   return;
+}
+
+/**
+ * This function prints the current
+ * weights of the neural network.
+ */
+void printWeights()
+{
+   printf("\nNOW PRINTING WEIGHTS\n");
+   for (int i = 0; i < totalWeights; i++)
+   {
+      printf("%lf\n", weights[i]);
+   }
+
+   return;
+}
+
+/**
+ * Trains the network once for all training sets, using backprop,
+ * then calculates the new error.
+ * 
+ * Adaptive learning can be disabled by setting the learning
+ * factor scaler to 1.0 in the config. Weight rollback can 
+ * also be enabled/disabled.
+ */
+void trainForAllTrainingSets()
+{
+   double *oldWeights;
+   // only enable weight rollback if adaptive learning is enabled as well
+   if (enableWeightRollback == 'Y' && learningFactorScaler != 1.0)
+   {
+      oldWeights = calloc(totalWeights, sizeof(double));
+      for (int i = 0; i < totalWeights; i++)
+      {
+         oldWeights[i] = weights[i]; // storing old weights
+      }
+   }
+
+   double errorSum = 0.0;
+   int index = 0;
+   for (int t = 0; t < numTrainingSets; t++) // train on every training set
+   {
+      for (int k = 0; k < numInputNodes; k++) // setting correct input values
+      {
+         nodes[k] = trainingSets[index];
+         index++;
+      }
+      for (int k = 0; k < numOutputNodes; k++) // setting correct expected output values
+      {
+         expectedOutputs[k] = trainingSets[index];
+         index++;
+      }
+
+      runNetwork();
+
+      // collecting/applying psi values in the rightmost layer
+      for (int j = layerDimensions[numLayers - 2] - 1; j >= 0; j--) // last hidden layer
+      {
+         int sourceNodeIndex = maxNodesInALayer * (numLayers - 2) + j;
+
+         for (int i = layerDimensions[numLayers - 1] - 1; i >= 0; i--) // output layer
+         {
+            int destNodeIndex = maxNodesInALayer * (numLayers - 1) + i;
+
+            int weightJIIndex = maxWeightsInALayer * (numLayers - 2) + maxNodesInALayer * j + i;
+
+            double w = nodes[destNodeIndex] - expectedOutputs[i];
+            double theta = thetas[maxNodesInALayer * (numLayers - 1) + i];
+            double psiI = w * outputDerivFunction(theta);
+
+            psis[destNodeIndex] = psiI;
+            psis[maxNodesInALayer * (numLayers - 2) + j] += psiI * weights[weightJIIndex];
+
+            /**
+             * A -= is used here instead of a += like the documentation states
+             * because when the weights are calculated, they are not multiplied
+             * by the the extra -1 in the calculation formula. This avoids
+             * unnecessarily flipping signs two times, saving time.
+             */ 
+            weights[weightJIIndex] -= learningFactor * nodes[sourceNodeIndex] * psiI;
+
+         } // for (int i = layerDimensions[numLayers - 1] - 1; i >= 0; i--)
+
+         double thetaJ = thetas[maxNodesInALayer * (numLayers - 2) + j];
+         psis[maxNodesInALayer * (numLayers - 2) + j] *= outputDerivFunction(thetaJ);
+      } // for (int j = layerDimensions[numLayers - 2] - 1; j >= 0; j--)
+
+      // collecting/applying values in the non-rightmost layers
+      for (int m = numLayers - 3; m >= 0; m--) // looping backwards through connectivity layers
+      {
+         int numSourceNodes = layerDimensions[m];
+         int numDestNodes = layerDimensions[m + 1];
+
+         for (int j = numDestNodes - 1; j >= 0; j--) // looping through right layer
+         {
+            int destNodeIndex = maxNodesInALayer * (m + 1) + j;
+
+            for (int k = numSourceNodes - 1; k >= 0; k--) // looping through left layer
+            {
+               int sourceNodeIndex = maxNodesInALayer * m + k;
+
+               double psiJ = psis[destNodeIndex];
+               int weightKJIndex = maxWeightsInALayer * m + maxNodesInALayer * k + j;
+
+               weights[weightKJIndex] -= learningFactor * nodes[sourceNodeIndex] * psiJ;
+
+            } // for (int k = numSourceNodes - 1; k >= 0; k--)
+         }    // for (int j = numDestNodes - 1; j >= 0; j--)
+      }       // for (int m = numLayers - 3; m >= 0; m--)
+
+      double err = calculateError();
+
+      errorSum += err * err;
+   }          // for (int t = 0; t < numTrainingSets; t++)
+
+   double newError = 0.5 * errorSum; // multiply by 0.5 according to the error function
+
+   if (learningFactorScaler != 1.0) // enable adaptive learning
+   {
+      if (newError > error && learningFactor > minLearningFactor) // error went up and the learning factor has room to decrease
+      {
+         learningFactor /= learningFactorScaler;
+
+         if (enableWeightRollback == 'Y')
+         {
+            for (int i = 0; i < totalWeights; i++)
+            {
+               weights[i] = oldWeights[i];
+            }
+            free(oldWeights);
+         }
+      }
+      else if (newError < error) // error went down
+      {
+         error = newError;
+         learningFactor *= learningFactorScaler;
+      }
+
+      if (learningFactor > maxLearningFactor)
+         learningFactor = maxLearningFactor; // capping the learning factor
+   }
+   else // adaptive learning is disabled
+   {
+      error = newError;
+   }
+
+   return;
+}
+
+/**
+ * This runs the network for all the training sets and prints
+ * out the input nodes, output nodes, expected output nodes, and error.
+ * It also prints out the total error over all training sets.
+ * No training is done.
+ */
+void runForAllTrainingSets()
+{
+   int index = 0;
+   double errorSum = 0.0;
+
+   for (int i = 0; i < numTrainingSets; i++) // looping through all training sets
+   {
+      for (int k = 0; k < numInputNodes; k++)
+      {
+
+         if (printNetworkSpecifics == 'Y') // for debugging
+         {
+            printf("%lf ", trainingSets[index]);
+         }
+
+         nodes[k] = trainingSets[index];
+         index++;
+      }
+      if (printNetworkSpecifics == 'Y')
+      {
+         printf(" --> (expected ");
+      }
+
+      for (int k = 0; k < numOutputNodes; k++)
+      {
+         
+         if (printNetworkSpecifics == 'Y') // for debugging
+         {
+            printf(" %lf", trainingSets[index]);
+         }
+
+         expectedOutputs[k] = trainingSets[index];
+         index++;
+      }
+
+      runNetwork();
+      double err = calculateError();
+
+      if (printNetworkSpecifics == 'Y') // for debugging
+      {
+         printf(") actual: ");
+         for (int k = 0; k < numOutputNodes; k++)
+         {
+            printf(" %lf", nodes[maxNodesInALayer * (numLayers - 1) + k]);
+         }
+         printf("\n");
+      }
+
+      errorSum += err * err;
+   }
+
+   error = errorSum * 0.5; // multiply by 0.5 according to the error function
+
+   printf("Total error: %.16lf\n\n", error);
+
+   return;
+}
+
+/**
+ * Trains the network.
+ * 
+ * @param numTimes the amount of times to train the network
+ * @param targetError the error at which to stop training (if reached)
+ */
+void train(int numTimes, double targetError)
+{
+   int cycles = 0;
+   while (cycles < numTimes && error > targetError)
+   {
+      trainForAllTrainingSets();
+      cycles++;
+
+      if (printDebugMessages == 'Y')
+      {
+         printf("DEBUG: iteration %d, error: %.16lf, lambda: %lf\n", cycles, error, learningFactor);
+      }
+
+      if (cycles % dumpEveryIterations == 0) // dumps values every _x_ iterations
+      {
+         writeWeightsToFile();
+         writeOutputsToFile();
+      }
+   }
+
+   runForAllTrainingSets();
+
+   printf("lambda: %lf\n", learningFactor);
+   printf("Stopped after %d cycles (max %d cycles)\n", cycles, numTimes);
+   printf("Current error: %.16lf\n", error);
+
+   // printing termination conditions that were or were not met
+   if (cycles == numTimes - 1)
+      printf("Stopped due to cycle amount\n");
+   if (error <= targetError)
+      printf("Stopped due to sufficiently low error (%.16lf < %.16lf)\n", error, targetError);
+   else
+      printf("Did not reach specified error successfully (%.16lf > %.16lf)\n", error, targetError);
+
+   return;
+}
\ No newline at end of file
diff --git a/outputFunctions.c b/outputFunctions.c
new file mode 100644
index 0000000..0f78ae5
--- /dev/null
+++ b/outputFunctions.c
@@ -0,0 +1,99 @@
+/**
+ * Gloria Zhu
+ * Created 9/8/2019
+ * This file holds a collection of output functions as well as their derivatives
+ * to be used in a neural network. Note that every function here should
+ * take in as a parameter a single float value, which it will
+ * return the corresponding output or derivative for.
+ * 
+ * Functions in this file:
+ * 
+ * sigmoid & sigmoidDeriv
+ * tanh & tanhDeriv
+ * relu & reluDeriv
+ */
+
+#include "./headerfiles/outputFunctions.h"
+
+#include <stdlib.h>
+#include <math.h>
+
+/**
+ * The sigmoid function is defined as:
+ * sigmoid(x) = 1/(1+e^-x)
+ * It returns values close to 1 for large
+ * values of x and values close to 
+ * 0 for small values of x.
+ */
+double sigmoid(double value)
+{
+   return 1.0 / (1.0 + exp(-value));
+}
+
+/**
+ * This function returns the derivative of the sigmoid function,
+ * which is sigmoid(x) * (1-sigmoid(x))
+ */ 
+double sigmoidDeriv(double value)
+{
+   double sig = sigmoid(value);
+   return sig * (1.0 - sig);
+}
+
+/**
+ * The hyperbolic tangent function is defined as:
+ * tanh(x) = sinh(x)/cosh(x)
+ * where sinh(x) = ( e^x - e^-x )/2
+ * and cosh(x) = ( e^x + e^-x )/2
+ * It returns values close to 1 for large
+ * values of x and values close to
+ * -1 for small values of x.
+ */
+double tanh(double value)
+{
+   return tanh(value);
+}
+
+/**
+ * This function returns the derivative of the tanh function,
+ * which is sech(x)^2, or 1/cosh(x)^2
+ */ 
+double tanhDeriv(double value)
+{
+   return 1.0 / (cosh(value) * cosh(value));
+}
+
+/**
+ * The ReLU (rectified linear activation unit) function is defined as:
+ * relu(x) =   x if x>0
+ *             0 if x<=0
+ * It returns the value itself if the value is positive
+ * and zero if the value is negative.
+ */
+double relu(double value)
+{
+   return fmax(0.0, value);
+}
+
+/**
+ * This function returns the derivative of the relu function,
+ * which is 0 if the value is negative and 1 if the value is positive.
+ * 
+ * Note that the derivative does not exist at x=0 since the left/right
+ * derivatives are different; in this case, the function merely returns 1.
+ * 
+ * Please excuse the use of two return statements; this seemed like the most 
+ * efficient way to write this function.
+ */ 
+double reluDeriv(double value)
+{
+   if (value>=0.0)
+   {
+      return 1.0;
+   }
+   else
+   {
+      return 0.0;
+   }
+   
+}
\ No newline at end of file
diff --git a/weights/placeholder.txt b/weights/placeholder.txt
new file mode 100644
index 0000000..e69de29