//  Copyright 2013 Google Inc. All Rights Reserved.
//
//  Licensed under the Apache License, Version 2.0 (the "License");
//  you may not use this file except in compliance with the License.
//  You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
//  Unless required by applicable law or agreed to in writing, software
//  distributed under the License is distributed on an "AS IS" BASIS,
//  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//  See the License for the specific language governing permissions and
//  limitations under the License.

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <pthread.h>

#define MAX_STRING 100
#define EXP_TABLE_SIZE 1000
#define MAX_EXP 6
#define MAX_SENTENCE_LENGTH 1000
#define MAX_CODE_LENGTH 40

const int vocab_hash_size = 30000000;  // Maximum 30 * 0.7 = 21M words in the vocabulary

typedef float real;                    // Precision of float numbers

struct vocab_word
{
        long long cn;// The frequence of the word occurance
        int *point; // Parent nodes sequence in up-to-down order, the index of root node is vocab_size*2-2
        char *word, *code, codelen;// Code means is 0-1 sequence in up-to-down order
};

char train_file[MAX_STRING], output_file[MAX_STRING];
char save_vocab_file[MAX_STRING], read_vocab_file[MAX_STRING];
struct vocab_word *vocab; // sorted vocabulary
int binary = 0, cbow = 0, debug_mode = 2, window = 5, min_count = 5, num_threads = 1, min_reduce = 1;
int *vocab_hash; // this is used to find the index in uppper vocab array according the hash
long long vocab_max_size = 1000, vocab_size = 0, layer1_size = 100; // layer1_size mean hidden layer neurons number
long long train_words = 0, word_count_actual = 0, file_size = 0, classes = 0;
real alpha = 0.025, starting_alpha, sample = 0;
real *syn0, *syn1, *syn1neg, *expTable;
clock_t start;

int hs = 1, negative = 0;
const int table_size = 1e8;
int *table;

void InitUnigramTable() // the computed table is used for negative sampling
{
        int a, i;
        long long train_words_pow = 0;
        real d1, power = 0.75;
        table = (int *)malloc(table_size * sizeof(int));
        for (a = 0; a < vocab_size; a++) train_words_pow += pow(vocab[a].cn, power);

        i = 0;
        // If train_words_pow is quite large, some low frequency words will be ignored
        d1 = pow(vocab[i].cn, power) / (real)train_words_pow;
        for (a = 0; a < table_size; a++)
    {
                table[a] = i;

                if (a / (real)table_size > d1)
                {
                        i++;
                        d1 += pow(vocab[i].cn, power) / (real)train_words_pow;
                }

                if (i >= vocab_size) i = vocab_size - 1;
        }
}

// Reads a single word from a file, assuming space + tab + EOL to be word boundaries
void ReadWord(char *word, FILE *fin) 
{
        int a = 0, ch;
        while (!feof(fin)) 
    {
                ch = fgetc(fin);
                if (ch == 13) continue;
                if ((ch == ' ') || (ch == '\t') || (ch == '\n')) 
        {
                        if (a > 0) 
            {
                                if (ch == '\n') ungetc(ch, fin);

                                break;
                        }

                        if (ch == '\n') 
            {
                                strcpy(word, (char *)"</s>");

                                return;
                        } else continue;
                }

                word[a] = ch;
                a++;
                if (a >= MAX_STRING - 1) a--;   // Truncate too long words
        }

        word[a] = 0;
}

// Returns hash value of a word
int GetWordHash(char *word)
{
        unsigned long long a, hash = 0;
        for (a = 0; a < strlen(word); a++) hash = hash * 257 + word[a];

        hash = hash % vocab_hash_size;
        return hash;
}

// Returns index of a word in the vocabulary, not hash; if the word is not found, returns -1
int SearchVocab(char *word)
{
        unsigned int hash = GetWordHash(word);
        while (1)
        {
                if (vocab_hash[hash] == -1) return -1;
                if (!strcmp(word, vocab[vocab_hash[hash]].word))
                        return vocab_hash[hash];
                hash = (hash + 1) % vocab_hash_size;
        }

        return -1;
}

// Reads a word and returns its index in the vocabulary
int ReadWordIndex(FILE *fin)
{
        char word[MAX_STRING];
        ReadWord(word, fin);
        if (feof(fin)) return -1;
        return SearchVocab(word);
}

// Adds a word to the vocabulary
int AddWordToVocab(char *word)
{
        unsigned int hash, length = strlen(word) + 1;
        if (length > MAX_STRING) length = MAX_STRING;

        vocab[vocab_size].word = (char *)calloc(length, sizeof(char));
        strcpy(vocab[vocab_size].word, word);
        vocab[vocab_size].cn = 0;
        vocab_size++;

        // Reallocate memory if needed
        if (vocab_size + 2 >= vocab_max_size) // initial vocab_max_size is 1000
        {
                vocab_max_size += 1000;
                vocab = (struct vocab_word *)realloc(vocab, vocab_max_size * sizeof(struct vocab_word));
        }

        hash = GetWordHash(word);

    // if hash collides
        while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
        vocab_hash[hash] = vocab_size - 1;

        return vocab_size - 1;
}

// Used later for sorting by word counts
int VocabCompare(const void *a, const void *b)
{
        return ((struct vocab_word *)b)->cn - ((struct vocab_word *)a)->cn;
}

// Sorts the vocabulary by frequency using word counts, and re-compute the hash
void SortVocab()
{
        int a, size;
        unsigned int hash;

        // Sort the vocabulary and keep </s> at the first position
        qsort(&vocab[1], vocab_size - 1, sizeof(struct vocab_word), VocabCompare);
        for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
        size = vocab_size;
        train_words = 0;
        for (a = 0; a < size; a++)
        {
                // Words occuring less than min_count times will be discarded from the vocab
                if (vocab[a].cn < min_count)
                {
                        vocab_size--;
                        free(vocab[vocab_size].word);
                }
                else
                {
                        // Hash will be re-computed, as after the sorting it is not actual
                        hash=GetWordHash(vocab[a].word);
                        while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
                        vocab_hash[hash] = a;
                        train_words += vocab[a].cn;
                }
        }

        // Allocate memory for the binary tree construction
        vocab = (struct vocab_word *)realloc(vocab, (vocab_size + 1) * sizeof(struct vocab_word));
        for (a = 0; a < vocab_size; a++)
        {
                vocab[a].code = (char *)calloc(MAX_CODE_LENGTH, sizeof(char));
                vocab[a].point = (int *)calloc(MAX_CODE_LENGTH, sizeof(int));
        }
}

// Reduces the vocabulary by removing infrequent tokens
void ReduceVocab()
{
        int a, b = 0;
        unsigned int hash;
        for (a = 0; a < vocab_size; a++)
    {
                if (vocab[a].cn > min_reduce)
                {
                        vocab[b].cn = vocab[a].cn;
                        vocab[b].word = vocab[a].word;
                        b++;
                }
                else free(vocab[a].word);
    }

        vocab_size = b;
        for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
        for (a = 0; a < vocab_size; a++) 
    {
                // Hash will be re-computed, as it is not actual
                hash = GetWordHash(vocab[a].word);
                while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
                vocab_hash[hash] = a;
        }

        fflush(stdout);
        min_reduce++;
}

// Create binary Huffman tree using the word counts
// Frequent words will have short uniqe binary codes
void CreateBinaryTree() 
{
        long long a, b, i, min1i, min2i, pos1, pos2, point[MAX_CODE_LENGTH];
        char code[MAX_CODE_LENGTH];
        long long *count = (long long *)calloc(vocab_size * 2 + 1, sizeof(long long));
        long long *binary = (long long *)calloc(vocab_size * 2 + 1, sizeof(long long));
        long long *parent_node = (long long *)calloc(vocab_size * 2 + 1, sizeof(long long));

        for (a = 0; a < vocab_size; a++) count[a] = vocab[a].cn;
        for (a = vocab_size; a < vocab_size * 2; a++) count[a] = 1e15;

        pos1 = vocab_size - 1;
        pos2 = vocab_size;

        for (a = 0; a < vocab_size - 1; a++) // Following algorithm constructs the Huffman tree by adding one node at a time
        {
                // First, find two smallest nodes 'min1, min2'
                if (pos1 >= 0)
                {
                        if (count[pos1] < count[pos2])
                        {
                                min1i = pos1;
                                pos1--;
                        }
                        else
                        {
                                min1i = pos2;
                                pos2++;
                        }
                }
                else
                {
                        min1i = pos2;
                        pos2++;
                }

                if (pos1 >= 0)
                {
                        if (count[pos1] < count[pos2])
                        {
                                min2i = pos1;
                                pos1--;
                        }
                        else
                        {
                                min2i = pos2;
                                pos2++;
                        }
                }
                else
                {
                        min2i = pos2;
                        pos2++;
                }

                count[vocab_size + a] = count[min1i] + count[min2i];
                parent_node[min1i] = vocab_size + a; // Parent node of min1i and min2i is node vocab_size+a
                parent_node[min2i] = vocab_size + a;
                binary[min2i] = 1; // binary[min1i] = 0
        }

        // Now assign binary code to each vocabulary word
        for (a = 0; a < vocab_size; a++)
        {
                b = a;
                i = 0;
                while (1)
                {
                        code[i] = binary[b]; // binary[0] is the code of the leaf node
                        point[i] = b;           // point[0] is the index of the leaf node itself
                        i++;
                        b = parent_node[b]; // Get the parent node index of node b
                        if (b == vocab_size * 2 - 2) break; // The root node index vocab_size*2-2
                }

                vocab[a].codelen = i;
                vocab[a].point[0] = vocab_size - 2; // The index of root node is vocab_size*2-2, here we can see that the point value parent_node-vocab_size
                for (b = 0; b < i; b++)
                {
                        vocab[a].code[i - b - 1] = code[b]; // code is the binary code of the word in huffman tree
                        vocab[a].point[i - b] = point[b] - vocab_size; // this is tricky
                }
        }

        free(count);
        free(binary);
        free(parent_node);
}

void LearnVocabFromTrainFile()
{
        char word[MAX_STRING];
        FILE *fin;
        long long a, i;
        for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1; // here vocab_hash_size is 30000000

        fin = fopen(train_file, "rb");
        if (fin == NULL)
        {
                printf("ERROR: training data file not found!\n");
                exit(1);
        }

        vocab_size = 0;
        AddWordToVocab((char *)"</s>"); // this is the first word in vocab
        while (1)
        {
                ReadWord(word, fin);
                if (feof(fin)) break;
                train_words++;
                if ((debug_mode > 1) && (train_words % 100000 == 0)) // output the progress
                {
                        printf("%lldK%c", train_words / 1000, 13);
                        fflush(stdout);
                }

                i = SearchVocab(word);
                if (i == -1) // it's a new word
                {
                        a = AddWordToVocab(word);// add word to the vocab and return the sequence id since first word added
                        vocab[a].cn = 1;
                }
        else vocab[i].cn++;

        if (vocab_size > vocab_hash_size * 0.7) ReduceVocab(); // remove some low frequency words
        }

        SortVocab();
        if (debug_mode > 0)
        {
                printf("Vocab size: %lld\n", vocab_size);
                printf("Words in train file: %lld\n", train_words);
        }

        file_size = ftell(fin);
        fclose(fin);
}

void SaveVocab() {
        long long i;
        FILE *fo = fopen(save_vocab_file, "wb");
        for (i = 0; i < vocab_size; i++) fprintf(fo, "%s %lld\n", vocab[i].word, vocab[i].cn);
        fclose(fo);
}

// read vocab processed before
void ReadVocab()
{
        long long a, i = 0;
        char c;
        char word[MAX_STRING];
        FILE *fin = fopen(read_vocab_file, "rb");
        if (fin == NULL)
        {
                printf("Vocabulary file not found\n");
                exit(1);
        }

        for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;

        vocab_size = 0;
        while (1)
        {
                ReadWord(word, fin);
                if (feof(fin)) break;
                a = AddWordToVocab(word); // then the hash address is returned

                fscanf(fin, "%lld%c", &vocab[a].cn, &c);
                i++;
        }

        SortVocab();// sort the vocab with respect to the word frequency
        if (debug_mode > 0)
        {
                printf("Vocab size: %lld\n", vocab_size);
                printf("Words in train file: %lld\n", train_words);
        }

        fin = fopen(train_file, "rb");
        if (fin == NULL)
        {
                printf("ERROR: training data file not found!\n");
                exit(1);
        }

        fseek(fin, 0, SEEK_END);
        file_size = ftell(fin);
        fclose(fin);
}

// initial connection weights, syn1 and syn1neg set to 0 and syn0 ranging from -0.5 to 0.5
void InitConnectionWeights()
{
        long long a, b;
        a = posix_memalign((void **)&syn0, 128, (long long)vocab_size * layer1_size * sizeof(real));
        if (syn0 == NULL)
        {
                printf("Memory allocation failed\n"); exit(1);
        }

        if (hs)
        {
                a = posix_memalign((void **)&syn1, 128, (long long)vocab_size * layer1_size * sizeof(real));
                if (syn1 == NULL)
                {
                        printf("Memory allocation failed\n"); exit(1);
                }
                for (b = 0; b < layer1_size; b++)
                        for (a = 0; a < vocab_size; a++)
                                syn1[a * layer1_size + b] = 0;
        }

        if (negative>0)
        {
                a = posix_memalign((void **)&syn1neg, 128, (long long)vocab_size * layer1_size * sizeof(real));
                if (syn1neg == NULL)
                {
                        printf("Memory allocation failed\n"); exit(1);
                }
                for (b = 0; b < layer1_size; b++)
                        for (a = 0; a < vocab_size; a++)
                                syn1neg[a * layer1_size + b] = 0;
        }

        for (b = 0; b < layer1_size; b++)
                for (a = 0; a < vocab_size; a++)
                        syn0[a * layer1_size + b] = (rand() / (real)RAND_MAX - 0.5) / layer1_size; // ranging from -0.5 to 0.5, the mean is 0
}

// Before this step, all the vocabs are sorted by frequency and encoded with Huffman tree, the connection weights are also initialed
void *TrainModelThread(void *id)
{
        long long a, b, d; // counters
    long long word; // the target word
    long long last_word; // words surrounds the target word
    long long sentence_length = 0, sentence_position = 0; //
        long long word_count = 0; // number of words have read from train file
    long long last_word_count = 0; // before this, the number of words read from train file
    long long sen[MAX_SENTENCE_LENGTH + 1]; // read 1000 words from file and only one processed each round
        long long l1, l2, c, target, label; // explained latter
        unsigned long long next_random = (long long)id, multiplier=25214903917; // initially, next_random is the id of worker thread, search wiki with keyword LCG for more info    
        real f, g; // explained latter
        clock_t now;
        real *neu1 = (real *)calloc(layer1_size, sizeof(real)); // this is the hidden layer
        real *neu1e = (real *)calloc(layer1_size, sizeof(real));
        FILE *fi = fopen(train_file, "rb");

        fseek(fi, file_size / (long long)num_threads * (long long)id, SEEK_SET);
        while (1) // Each round, only one word is trained, read 1000 words if previous words are all trained.
        {
                if (word_count - last_word_count > 10000) // print the progress
                {
                        word_count_actual += word_count - last_word_count; // Some frequent words are discarded, however, which need to be counted as well.
                        last_word_count = word_count;
                        if ((debug_mode > 1))
                        {
                                now=clock();
                                printf("%cAlpha: %f  Progress: %.2f%%  Words/thread/sec: %.2fk  ", 13, alpha,
                                                word_count_actual / (real)(train_words + 1) * 100,
                                                word_count_actual / ((real)(now - start + 1) / (real)CLOCKS_PER_SEC * 1000));
                                fflush(stdout);
                        }

                        alpha = starting_alpha * (1 - word_count_actual / (real)(train_words + 1));
                        if (alpha < starting_alpha * 0.0001) alpha = starting_alpha * 0.0001;
                }

                if (sentence_length == 0) // read MAX_SENTENCE_LENGTH = 1000 words from file, sub-sampling is applied
                {
                        while (1)
                        {
                                word = ReadWordIndex(fi);
                                if (feof(fi)) break;
                                if (word == -1) continue;
                                word_count++; // including the subsampled words
                                if (word == 0) break;

                                // The sub-sampling randomly discards frequent words while keeping the ranking same
                                if (sample > 0)
                                {
                                        // ran = (0.01*sqrt(x)+0.0001)/x, x = vocab[word].cn/train_words, x = 0.0001, ran -> 0.4, x = 1, ran - > 0.0101
                                        real ran = (sqrt(vocab[word].cn / (sample * train_words)) + 1) * (sample * train_words) / vocab[word].cn;

                                        next_random = next_random * multiplier + 11; 
                                        if (ran < (next_random & 0xFFFF) / (real)65536) continue; // if x is too large, ran will be small, there are good chances the word will be ignored
                                }

                                sen[sentence_length] = word;
                                sentence_length++; // exclude the subsampled words

                                if (sentence_length >= MAX_SENTENCE_LENGTH) break; // MAX_SENTENCE_LENGTH = 1000
                        }

                        sentence_position = 0; // pointer of the sentence
                }

                if (feof(fi)) break;
                if (word_count > train_words / num_threads) break; // if the thread has done trainning the words assigned to it, will exits.

                word = sen[sentence_position]; // get the target word
                if (word == -1) continue;

                for (c = 0; c < layer1_size; c++) neu1[c] = 0; // initial the hidden layer
                for (c = 0; c < layer1_size; c++) neu1e[c] = 0; // initial the back-propagation error

                next_random = next_random * multiplier + 11;
                b = next_random % window; // so the window will be dynamically changed for each word

                if (cbow) //train the cbow architecture of a single word sen[sentence_position]
                {
            // compute the hidden layer
                        for (a = b; a < window * 2 + 1 - b; a++)
            {
                                if (a != window)// scan the words around it, itself excluded
                                {
                    // sentence_position < c < sentence_position+window+1-b, c != sentence_position, if c=sentence_position, a=window
                                        // If sentence_position < b, only 'sentence_position' words will be scanned
                                        c = sentence_position - window + a; 
                                        if (c < 0) continue;

                                        // If sentence_position > sentence_length - b, only sentence_length - sentence_position words will be scanned
                                        if (c >= sentence_length) continue;

                                        last_word = sen[c];
                                        if (last_word == -1) continue; // if last word not exists in vocab, then continue

                                        for (c = 0; c < layer1_size; c++) neu1[c] += syn0[c + last_word * layer1_size]; // hidden layer not squashed

                                        // syn0 is vocab_size*layer1_size matrix, last_word is the index of last word, it is greater than 0 and less than vocab_size
                                        // neu1 matrix is the result of vector_neighbors.dot(syn0), where neighbors_vector is a 1*vocab_size vector,
                    // and the column value is 1 if it's the neighbor of sen[sentence_position], otherwise 0, same as one-hot presentation
                                        // Given that syn0 is a matrix of vocab_size*layer1_size, neu1 is all the sum of syn0[nearby_words * layer_size],
                    // which means that all the neighbor word vectors are summed up and become one vector
                                }
            }

                        if (hs) // Train with correct result, hidden -> output
                        {
                    for (d = 0; d < vocab[word].codelen; d++)// iterate all the parent nodes of word in up-to-down order, first node is the root node
                                {
                                        f = 0;
                                        l2 = vocab[word].point[d] * layer1_size; // find the predicted label in output layer by index 'vocab[word].point[d]'

                    // matrix dot production neu1.dot(syn1[l2]), syn1[l2] means l2 row of matrix syn1
                                        for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1[c + l2]; 
                    // if uppper statement expressed as output = neu1.dot(syn1), f = output[vocab[word].point[d]]            

                                        // if -inft --> f --> 0, then 0 --> xx --> 500, then 0 --> f' --> 1/2, otherwise 0 --> f --> +inft, then 1/2 --> f' --> 1
                                        if (f <= -MAX_EXP || f >= MAX_EXP) continue;
                                        else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]; // xx = 500 + f*500/MAX_EXP, f=x/(1+x), processed by activation function

                                        // 'g' is the gradient multiplied by the learning rate, and 1 -vocal[word].code[d] - f can be considered as the gradient since they are much close
                                        // if code is 0, f are expected to be 1 and thus code 0 means positive, I mean if code is 0, predicted label should be 1
                                        // if true label = 1 which mean 1 - vocab[word].code[d] = 1, when f < 1, means g > 0, then syn1[c + l2] increased, and vice versa.
                                        g = (1 - vocab[word].code[d] - f) * alpha;

                                        // Learn weights hidden -> output, use hidden layer neu1 update connection weights syn1
                                        // If vocab[word].code[d]=1 and f = 1, g < 0, means that f is too large, and how to reduce f? make neu1.dot(syn1[l2]) less,
                                        // so syn1[l2] = syn1[l2] + g*neu1 will help?, yes, of course, here f = neu1.dot(syn1[l2]+g*neu1)=neu1.dot(syn1[l2])+g*neu1.dot(neu1),
                                        // if g < 0, f is decreased, and vice versa.
                                        for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * neu1[c];

                                        // Propagate errors output -> hidden, this is used to update connection weights syn0
                                        for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];
                                }
            }

                        if (negative > 0) // Consider negative to be 5, train with error results
            {
                                for (d = 0; d < negative + 1; d++)
                                {
                                        if (d == 0)
                                        {
                                                target = word;
                                                label = 1; // the value can be whatever you like
                                        }else
                                        {
                                                next_random = next_random * multiplier + 11;
                                                target = table[(next_random >> 16) % table_size]; // table_size is 1e8

                                                if (target == 0) target = next_random % (vocab_size - 1) + 1; // target 0 is '<s>'
                                                if (target == word) continue;

                                                label = 0; // the value can be whatever you like
                                        }

                                        l2 = target * layer1_size;
                                        f = 0;
                                        for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1neg[c + l2];

                    // the prediction label is expected to be 0 as we define above
                                        if (f > MAX_EXP) g = (label - 1) * alpha;
                                        else if (f < -MAX_EXP) g = (label - 0) * alpha;
                                        else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;

                                        for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
                                        for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * neu1[c];
                                }
            }

                        for (a = b; a < window * 2 + 1 - b; a++)
            {
                                if (a != window) // not the target word itself
                                {
                                        c = sentence_position - window + a;
                                        if (c < 0) continue;
                                        if (c >= sentence_length) continue;

                                        last_word = sen[c];// Get a word around the specific word
                                        if (last_word == -1) continue;

                                        for (c = 0; c < layer1_size; c++) syn0[c + last_word * layer1_size] += neu1e[c]; // mini-batch gradient descent
                                }
            }
                }
                else    //train skip-gram
                { 
                        for (a = b; a < window * 2 + 1 - b; a++)
            {
                                if (a != window)
                                {
                                        c = sentence_position - window + a;
                                        if (c < 0) continue;
                                        if (c >= sentence_length) continue;

                                        last_word = sen[c];
                                        if (last_word == -1) continue;

                                        l1 = last_word * layer1_size; // syn0[l1] is the weights for word 'last_word'

                                        for (c = 0; c < layer1_size; c++) neu1e[c] = 0;

                                        if (hs)
                    {
                                                for (d = 0; d < vocab[word].codelen; d++)
                                                {
                                                        f = 0;
                                                        l2 = vocab[word].point[d] * layer1_size; // syn1[l2] is the weights for word vocab[word].point[d], the parent of 'last_word'

                                                        for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1[c + l2]; // Only one word given, so input is a vector likes [0,1,0,0,0,...,0]

                            // processed by activation function
                                                        if (f <= -MAX_EXP) continue;
                                                        else if (f >= MAX_EXP) continue;
                                                        else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];

                                                        g = (1 - vocab[word].code[d] - f) * alpha; // 'g' is the gradient multiplied by the learning rate

                                                        // Propagate errors output -> hidden
                                                        for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];

                                                        // Learn weights hidden -> output
                                                        for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * syn0[c + l1];
                                                }
                    }

                                        if (negative > 0)
                    {
                                                for (d = 0; d < negative + 1; d++)
                                                {
                                                        if (d == 0)
                                                        {
                                                                target = word;
                                                                label = 1;
                                                        }
                                                        else
                                                        {
                                                                next_random = next_random * multiplier + 11;
                                                                target = table[(next_random >> 16) % table_size];
                                                                if (target == 0) target = next_random % (vocab_size - 1) + 1;
                                                                if (target == word) continue;
                                                                label = 0;
                                                        }

                                                        l2 = target * layer1_size;
                                                        f = 0;
                                                        for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1neg[c + l2];

                                                        if (f > MAX_EXP) g = (label - 1) * alpha;
                                                        else if (f < -MAX_EXP) g = (label - 0) * alpha;
                                                        else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;

                                                        for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
                                                        for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * syn0[c + l1];
                                                }
                    }

                                        // Learn weights input -> hidden
                                        for (c = 0; c < layer1_size; c++) syn0[c + l1] += neu1e[c];
                                }
            }
                }

                sentence_position++;
                if (sentence_position >= sentence_length)
                {
                        sentence_length = 0;
                }
        }

        fclose(fi);
        free(neu1);
        free(neu1e);
        pthread_exit(NULL);
}

void TrainModel()
{
        long a, b, c, d;
        FILE *fo;
        pthread_t *pt = (pthread_t *)malloc(num_threads * sizeof(pthread_t));
        printf("Starting training using file %s\n", train_file);
        starting_alpha = alpha;

        if (read_vocab_file[0] != 0) ReadVocab();
        else LearnVocabFromTrainFile();

        if (save_vocab_file[0] != 0) SaveVocab();
        if (output_file[0] == 0) return;

        // Below is quite important.

        CreateBinaryTree(); // build the huffman tree of the vocab

        InitConnectionWeights(); // initial neural network connection weights

        if (negative > 0) InitUnigramTable(); // the computed table is used for negative sampling, frequent words will be chosen

        start = clock();
        for (a = 0; a < num_threads; a++) pthread_create(&pt[a], NULL, TrainModelThread, (void *)a);
        for (a = 0; a < num_threads; a++) pthread_join(pt[a], NULL);

        fo = fopen(output_file, "wb");
        if (classes == 0) // output directly
        {
                // Save the word vectors
                fprintf(fo, "%lld %lld\n", vocab_size, layer1_size);
                for (a = 0; a < vocab_size; a++)
                {
                        fprintf(fo, "%s ", vocab[a].word);
                        if (binary)
                                for (b = 0; b < layer1_size; b++)
                                        fwrite(&syn0[a * layer1_size + b], sizeof(real), 1, fo);
                        else
                                for (b = 0; b < layer1_size; b++)
                                        fprintf(fo, "%lf ", syn0[a * layer1_size + b]);
                        fprintf(fo, "\n");
                }
        }
        else // clustering using k-means
        {
                // Run K-means on the word vectors
                int clcn = classes, iter = 10, closeid; // What does classes stand for? Maybe divide into 'classes' clusters.
                int *centcn = (int *)malloc(classes * sizeof(int));
                int *cl = (int *)calloc(vocab_size, sizeof(int));
                real closev, x;
                real *cent = (real *)calloc(classes * layer1_size, sizeof(real));
                for (a = 0; a < vocab_size; a++) cl[a] = a % clcn; // At first assign each word to random cluster
                for (a = 0; a < iter; a++)
                {
                        for (b = 0; b < clcn * layer1_size; b++) cent[b] = 0;
                        for (b = 0; b < clcn; b++) centcn[b] = 1;

                        for (c = 0; c < vocab_size; c++)
                        {
                                for (d = 0; d < layer1_size; d++) cent[layer1_size * cl[c] + d] += syn0[c * layer1_size + d];
                                centcn[cl[c]]++;
                        }

                        for (b = 0; b < clcn; b++)
                        {
                                closev = 0;
                                for (c = 0; c < layer1_size; c++)
                                {
                                        cent[layer1_size * b + c] /= centcn[b];
                                        closev += cent[layer1_size * b + c] * cent[layer1_size * b + c];
                                }

                                closev = sqrt(closev);
                                for (c = 0; c < layer1_size; c++) cent[layer1_size * b + c] /= closev;
                        }

                        for (c = 0; c < vocab_size; c++)
                        {
                                closev = -10;
                                closeid = 0;
                                for (d = 0; d < clcn; d++)
                                {
                                        x = 0;
                                        for (b = 0; b < layer1_size; b++)
                                                x += cent[layer1_size * d + b] * syn0[c * layer1_size + b];
                                        if (x > closev)
                                        {
                                                closev = x;
                                                closeid = d;
                                        }
                                }

                                cl[c] = closeid;
                        }
                }

                // Save the K-means classes
                for (a = 0; a < vocab_size; a++) fprintf(fo, "%s %d\n", vocab[a].word, cl[a]);

                free(centcn);
                free(cent);
                free(cl);
        }

        fclose(fo);
}

int ArgPos(char *str, int argc, char **argv)
{
        int a;
        for (a = 1; a < argc; a++) if (!strcmp(str, argv[a]))
        {
                if (a == argc - 1)
                {
                        printf("Argument missing for %s\n", str);
                        exit(1);
                }
                return a;
        }
        return -1;
}

int main(int argc, char **argv) 
{
        int i;
        if (argc == 1) 
    {
                printf("WORD VECTOR estimation toolkit v 0.1b\n\n");
                printf("Options:\n");
                printf("Parameters for training:\n");

                printf("\t-train <file>\n");
                printf("\t\tUse text data from <file> to train the model\n");

                printf("\t-output <file>\n");
                printf("\t\tUse <file> to save the resulting word vectors / word clusters\n");

                printf("\t-size <int>\n");
                printf("\t\tSet size of word vectors; default is 100\n");

                printf("\t-window <int>\n");
                printf("\t\tSet max skip length between words; default is 5\n");

                printf("\t-sample <float>\n");
                printf("\t\tSet threshold for occurrence of words. Those that appear with higher frequency");
                printf(" in the training data will be randomly down-sampled; default is 0 (off), useful value is 1e-5\n");

                printf("\t-hs <int>\n");
                printf("\t\tUse Hierarchical Softmax; default is 1 (0 = not used)\n");

                printf("\t-negative <int>\n");
                printf("\t\tNumber of negative examples; default is 0, common values are 5 - 10 (0 = not used)\n");

                printf("\t-threads <int>\n");
                printf("\t\tUse <int> threads (default 1)\n");

                printf("\t-min-count <int>\n");
                printf("\t\tThis will discard words that appear less than <int> times; default is 5\n");

                printf("\t-alpha <float>\n");
                printf("\t\tSet the starting learning rate; default is 0.025\n");

                printf("\t-classes <int>\n");
                printf("\t\tOutput word classes rather than word vectors; default number of classes is 0 (vectors are written)\n");

                printf("\t-debug <int>\n");
                printf("\t\tSet the debug mode (default = 2 = more info during training)\n");

                printf("\t-binary <int>\n");
                printf("\t\tSave the resulting vectors in binary moded; default is 0 (off)\n");

                printf("\t-save-vocab <file>\n");
                printf("\t\tThe vocabulary will be saved to <file>\n");

                printf("\t-read-vocab <file>\n");
                printf("\t\tThe vocabulary will be read from <file>, not constructed from the training data\n");

                printf("\t-cbow <int>\n");
                printf("\t\tUse the continuous bag of words model; default is 0 (skip-gram model)\n");

                printf("\nExamples:\n");
                printf("./word2vec -train data.txt -output vec.txt -debug 2 -size 200 -window 5 -sample 1e-4 -negative 5 -hs 0 -binary 0 -cbow 1\n\n");

                return 0;
        }

        output_file[0] = 0;
        save_vocab_file[0] = 0;
        read_vocab_file[0] = 0;

        if ((i = ArgPos((char *)"-size", argc, argv)) > 0) layer1_size = atoi(argv[i + 1]);
        if ((i = ArgPos((char *)"-train", argc, argv)) > 0) strcpy(train_file, argv[i + 1]);
        if ((i = ArgPos((char *)"-save-vocab", argc, argv)) > 0) strcpy(save_vocab_file, argv[i + 1]);
        if ((i = ArgPos((char *)"-read-vocab", argc, argv)) > 0) strcpy(read_vocab_file, argv[i + 1]);
        if ((i = ArgPos((char *)"-debug", argc, argv)) > 0) debug_mode = atoi(argv[i + 1]);
        if ((i = ArgPos((char *)"-binary", argc, argv)) > 0) binary = atoi(argv[i + 1]);
        if ((i = ArgPos((char *)"-cbow", argc, argv)) > 0) cbow = atoi(argv[i + 1]);
        if ((i = ArgPos((char *)"-alpha", argc, argv)) > 0) alpha = atof(argv[i + 1]);
        if ((i = ArgPos((char *)"-output", argc, argv)) > 0) strcpy(output_file, argv[i + 1]);
        if ((i = ArgPos((char *)"-window", argc, argv)) > 0) window = atoi(argv[i + 1]);
        if ((i = ArgPos((char *)"-sample", argc, argv)) > 0) sample = atof(argv[i + 1]);
        if ((i = ArgPos((char *)"-hs", argc, argv)) > 0) hs = atoi(argv[i + 1]);
        if ((i = ArgPos((char *)"-negative", argc, argv)) > 0) negative = atoi(argv[i + 1]);
        if ((i = ArgPos((char *)"-threads", argc, argv)) > 0) num_threads = atoi(argv[i + 1]);
        if ((i = ArgPos((char *)"-min-count", argc, argv)) > 0) min_count = atoi(argv[i + 1]);
        if ((i = ArgPos((char *)"-classes", argc, argv)) > 0) classes = atoi(argv[i + 1]);

        vocab = (struct vocab_word *)calloc(vocab_max_size, si

    // Precompute f(x) = x / (x + 1), f(x) = 1 / (1/x + 1)zeof(struct vocab_word));
        vocab_hash = (int *)calloc(vocab_hash_size, sizeof(int));
        expTable = (real *)malloc((EXP_TABLE_SIZE + 1) * sizeof(real));
        for (i = 0; i < EXP_TABLE_SIZE; i++) // EXP_TABLE_SIZE=1000 and MAX_EXP is 6
        {
                //expTable[i] = exp((i -500)/ 500 * 6), ranging e^-6 ~ e^6
                expTable[i] = exp((i / (real)EXP_TABLE_SIZE * 2 - 1) * MAX_EXP);

                // Precompute the exp() table, if 0 --> i --> 500, -6 --> exp --> 0, 0 --> x --> 1/2, then f(x) --> 0
        // otherwise, if 500 --> i --> 1000, then 1/2 --> f(x) --> 1

                expTable[i] = expTable[i] / (expTable[i] + 1);
        }

        TrainModel();

        return 0;
}
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