├── .gitignore ├── LICENSE ├── Makefile ├── README.md ├── gen-freqtab.py ├── gen-top-words.py ├── insert.py ├── top.txt └── vshow.c /.gitignore: -------------------------------------------------------------------------------- 1 | *.jsonl 2 | misc/ 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright (c) 2025, Salvatore Sanfilippo 2 | All rights reserved. 3 | 4 | Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 5 | 6 | * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 7 | * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 8 | 9 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 10 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | all: vshow 2 | 3 | vshow: vshow.c 4 | $(CC) vshow.c -O2 -Wall -W -o vshow 5 | 6 | clean: 7 | rm -rf vshow 8 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | This repository contains the code used in the following blog post and YouTube videos: 2 | 3 | * https://antirez.com/news/150 4 | * https://www.youtube.com/@antirez 5 | 6 | ## Where to get the HN archive Parquet files: 7 | 8 | I downloaded the files from this HF repo. Warning: 10GB of data. 9 | 10 | * https://huggingface.co/datasets/OpenPipe/hacker-news 11 | 12 | ## Original post that inspired this work: 13 | 14 | The initial analysis was performed by Christopher Tarry in 2022, then 15 | posted on Hacker News. Unfortunately the web site is now offline, but luckily the Internet Archive have a copy of the site about section and the output for the Paul Graham account: 16 | 17 | * https://news.ycombinator.com/item?id=33755016 18 | * https://web.archive.org/web/20221126204005/https://stylometry.net/about 19 | * https://web.archive.org/web/20221126235433/https://stylometry.net/user?username=pg 20 | 21 | ## Executing the scripts to populate Redis 22 | 23 | You need Redis 8 RC or greater in order to have the new data type, the Vector Sets. Once you have an instance running on your computer, do the following. 24 | 25 | Generate the top words list. We generate 10k, but we use a lot less later. 26 | 27 | python3 gen-top-words.py dataset/train*.parquet --count 10000 --output-file top.txt 28 | 29 | Turn the dataset into a JSONL file with just the username and the frequency talbe. This script will use quite some memory, since it needs to aggregate data by user, and will produce the output only at the end. Note also that this will generate the full frequency table for the user, all the words, not just the top words or the 350 words we use later. This way, the generated file can be used later for different goals and parameters (with 100 or 500 top words for instance). 30 | 31 | python3 gen-freqtab.py dataset/train*.parquet > freqtab.jsonl 32 | 33 | Finally we are ready to insert the data into a Redis vector set. 34 | 35 | python3 insert.py --top-words-file top.txt --top-words-count 350 freqtab.jsonl 36 | 37 | Now, start the `redis-cli`, and run something like: 38 | 39 | 127.0.0.1:6379> vsim hn_fingerprint ele pg 40 | 1) "pg" 41 | 2) "karaterobot" 42 | 3) "Natsu" 43 | 4) "mattmaroon" 44 | 5) "chc" 45 | 6) "montrose" 46 | 7) "jfengel" 47 | 8) "emodendroket" 48 | 9) "vintermann" 49 | 10) "c3534l" 50 | 51 | Use the `WITHSCORES` option if you want to see the similarity score. 52 | 53 | ## Using the vector visualization tool. 54 | 55 | This tool will display the vector as graphics in your terminal. You need to use Ghostty, Kitty or any other terminal supporting the Kitty terminal graphics protocol. There are other graphics protocols available, feel free to port this program to other terminals and send a PR, if you wish. 56 | 57 | Compile with `make`, then: 58 | 59 | redis-cli vemb hn_fingerprint | ./vshow 60 | 61 | -------------------------------------------------------------------------------- /gen-freqtab.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import json 3 | import sys 4 | import re 5 | import urllib.parse 6 | import argparse 7 | import multiprocessing as mp 8 | from functools import partial 9 | from html import unescape 10 | from bs4 import BeautifulSoup 11 | import pyarrow.parquet as pq 12 | import os 13 | import time 14 | from itertools import islice 15 | 16 | # Pre-compile regex patterns for better performance 17 | URL_PATTERN = re.compile(r'\[(https?://[^\s\]]+)\]|\b(https?://[^\s]+)\b') 18 | HTML_PATTERN = re.compile(r'<[a-zA-Z]+[^>]*>|&[a-zA-Z]+;') 19 | WORD_PATTERN = re.compile(r'\b[a-zA-Z][a-zA-Z\'-]*[a-zA-Z]\b|\b[a-zA-Z]\b') 20 | MULTI_NEWLINE_PATTERN = re.compile(r'\n{3,}') 21 | 22 | def extract_domain(url): 23 | """Extract domain name from a URL.""" 24 | if not url: 25 | return None 26 | 27 | try: 28 | # Handle special cases like relative URLs or malformed URLs 29 | if not url.startswith(('http://', 'https://')): 30 | if url.startswith('//'): 31 | url = 'http:' + url 32 | else: 33 | url = 'http://' + url 34 | 35 | # Parse URL 36 | parsed_url = urllib.parse.urlparse(url) 37 | 38 | # Get domain (remove www. if present) 39 | domain = parsed_url.netloc 40 | domain = re.sub(r'^www\.', '', domain) 41 | 42 | return domain 43 | except Exception: 44 | # If URL parsing fails, return the original string as fallback 45 | return url 46 | 47 | def create_frequency_table(text): 48 | """Create a frequency table of ALL words in the given text.""" 49 | if pd.isna(text) or not text: 50 | return {} 51 | 52 | # Initialize frequency table 53 | freq_table = {} 54 | 55 | # Function to replace URLs with their domain representation 56 | def replace_url(match): 57 | url = match.group(1) or match.group(2) 58 | domain = extract_domain(url) 59 | token = f"linkto:{domain}" 60 | 61 | # Add to frequency table 62 | freq_table[token] = freq_table.get(token, 0) + 1 63 | 64 | return f" {token} " 65 | 66 | # Replace URLs with tokens 67 | text = URL_PATTERN.sub(replace_url, text) 68 | 69 | # Tokenize the text - split by whitespace and filter out punctuation 70 | words = WORD_PATTERN.findall(text.lower()) 71 | 72 | # Count word frequencies - no filtering by stopwords or vocabulary 73 | for word in words: 74 | if len(word) > 0: # Keep all words, even single characters 75 | freq_table[word] = freq_table.get(word, 0) + 1 76 | 77 | return freq_table 78 | 79 | def clean_html(html_text): 80 | """Clean HTML from text, preserving paragraph structure and link information.""" 81 | if pd.isna(html_text): 82 | return None 83 | 84 | # If the text is not a string (somehow), convert it to string 85 | if not isinstance(html_text, str): 86 | return str(html_text) 87 | 88 | # Unescape HTML entities 89 | unescaped = unescape(html_text) 90 | 91 | # Only use BeautifulSoup if the text actually contains HTML-like patterns. 92 | # This speeds up computation since BS can be slow. 93 | if HTML_PATTERN.search(unescaped): 94 | # Use BeautifulSoup to parse and extract text 95 | soup = BeautifulSoup(unescaped, 'html.parser') 96 | 97 | # Replace

tags with newlines 98 | for p in soup.find_all('p'): 99 | p.replace_with('\n\n' + p.get_text() + '\n\n') 100 | 101 | # Extract text from links while preserving the URL 102 | for a in soup.find_all('a'): 103 | href = a.get('href', '') 104 | a.replace_with(f"{a.get_text()} [{href}]") 105 | 106 | cleaned_text = soup.get_text().strip() 107 | else: 108 | # If no HTML detected, just use the text as is 109 | cleaned_text = unescaped 110 | 111 | # Clean up extra whitespace 112 | cleaned_text = MULTI_NEWLINE_PATTERN.sub('\n\n', cleaned_text) 113 | 114 | return cleaned_text 115 | 116 | def merge_frequency_tables(table1, table2): 117 | """Merge two frequency tables, summing counts for common words.""" 118 | merged = table1.copy() 119 | for word, count in table2.items(): 120 | merged[word] = merged.get(word, 0) + count 121 | return merged 122 | 123 | def process_parquet_file(file_path): 124 | """Process a single parquet file and return user frequency tables.""" 125 | try: 126 | worker_id = mp.current_process().name 127 | print(f"[{worker_id}] Processing file: {file_path}", file=sys.stderr) 128 | start_time = time.time() 129 | 130 | # Local frequency table for this file 131 | local_user_freqtab = {} 132 | 133 | try: 134 | # Open the parquet file and read only the columns we need 135 | parquet_file = pq.ParquetFile(file_path) 136 | 137 | # Check if we can use predicate pushdown 138 | schema = parquet_file.schema 139 | has_type_column = 'type' in schema.names 140 | 141 | # Process the file in batches (row groups) 142 | try: 143 | # First try with filters (newer PyArrow versions) 144 | if has_type_column: 145 | batches = parquet_file.iter_batches( 146 | batch_size=1000, 147 | columns=['by', 'text', 'type'], 148 | filters=[('type', '=', 'comment')] 149 | ) 150 | else: 151 | batches = parquet_file.iter_batches( 152 | batch_size=1000, 153 | columns=['by', 'text'] 154 | ) 155 | except TypeError: 156 | # Fallback for older PyArrow versions without filter support 157 | print(f"[{worker_id}] Filter not supported, falling back to manual filtering", file=sys.stderr) 158 | if has_type_column: 159 | batches = parquet_file.iter_batches( 160 | batch_size=1000, 161 | columns=['by', 'text', 'type'] 162 | ) 163 | else: 164 | batches = parquet_file.iter_batches( 165 | batch_size=1000, 166 | columns=['by', 'text'] 167 | ) 168 | 169 | for batch in batches: 170 | # Convert batch to pandas DataFrame 171 | batch_df = batch.to_pandas() 172 | 173 | # Filter to only include comments if we couldn't use predicate pushdown 174 | if not has_type_column or 'type' not in batch_df.columns: 175 | # If 'type' column doesn't exist, assume all are comments 176 | comments_df = batch_df 177 | else: 178 | comments_df = batch_df[batch_df['type'] == 'comment'].copy() 179 | 180 | # Skip if no comments in this batch 181 | if comments_df.empty: 182 | continue 183 | 184 | # Process each comment 185 | for _, row in comments_df.iterrows(): 186 | # Skip if no user or text 187 | if pd.isna(row['by']) or pd.isna(row['text']): 188 | continue 189 | 190 | username = row['by'] 191 | 192 | # Clean the text 193 | cleaned_text = clean_html(row['text']) 194 | 195 | # Generate frequency table for this comment - no vocabulary or stopwords filtering 196 | comment_freqtab = create_frequency_table(cleaned_text) 197 | 198 | # If this is the first comment from this user, initialize their frequency table 199 | if username not in local_user_freqtab: 200 | local_user_freqtab[username] = comment_freqtab 201 | else: 202 | # Merge this comment's frequency table with the user's existing one 203 | local_user_freqtab[username] = merge_frequency_tables( 204 | local_user_freqtab[username], comment_freqtab 205 | ) 206 | 207 | except Exception as e: 208 | print(f"[{worker_id}] Error reading file '{file_path}': {e}", file=sys.stderr) 209 | return {} 210 | 211 | elapsed = time.time() - start_time 212 | print(f"[{worker_id}] Finished processing file: {file_path} in {elapsed:.2f} seconds", file=sys.stderr) 213 | print(f"[{worker_id}] Found {len(local_user_freqtab)} users in this file", file=sys.stderr) 214 | 215 | return local_user_freqtab 216 | 217 | except Exception as e: 218 | print(f"Error processing file '{file_path}': {e}", file=sys.stderr) 219 | return {} 220 | 221 | def worker_init(): 222 | """Initialize worker process.""" 223 | # Silence excessive logging in worker processes 224 | import logging 225 | logging.basicConfig(level=logging.WARNING) 226 | 227 | def merge_worker_results(results): 228 | """Merge frequency tables from multiple workers.""" 229 | merged_freqtab = {} 230 | 231 | # Merge user frequency tables from all files 232 | for user_freqtab in results: 233 | for username, freqtab in user_freqtab.items(): 234 | if username not in merged_freqtab: 235 | merged_freqtab[username] = freqtab 236 | else: 237 | merged_freqtab[username] = merge_frequency_tables( 238 | merged_freqtab[username], freqtab 239 | ) 240 | 241 | return merged_freqtab 242 | 243 | def chunks(data, size): 244 | """Split data into chunks of specified size.""" 245 | it = iter(data) 246 | for i in range(0, len(data), size): 247 | yield list(islice(it, size)) 248 | 249 | def write_results_to_file(user_freqtab, output_file=None): 250 | """Write results to file or stdout, sorting frequency tables by most frequent words.""" 251 | output = output_file if output_file else sys.stdout 252 | 253 | try: 254 | with open(output, 'w', encoding='utf-8') if isinstance(output, str) else output as f: 255 | for username, freqtab in user_freqtab.items(): 256 | # Sort the frequency table by count (most frequent first) 257 | sorted_freqtab = dict(sorted(freqtab.items(), key=lambda x: x[1], reverse=True)) 258 | 259 | # Create a record with just username and sorted frequency table 260 | record = { 261 | "by": username, 262 | "freqtab": sorted_freqtab 263 | } 264 | # Convert to JSON and write 265 | json_string = json.dumps(record) 266 | f.write(json_string + '\n') 267 | except Exception as e: 268 | print(f"Error writing results: {e}", file=sys.stderr) 269 | 270 | def main(): 271 | # Set up argument parser 272 | parser = argparse.ArgumentParser(description='Generate frequency tables from parquet files in parallel.') 273 | parser.add_argument('--workers', type=int, default=mp.cpu_count(), 274 | help='Number of worker processes (default: number of CPU cores)') 275 | parser.add_argument('--chunk-size', type=int, default=1, 276 | help='Number of files per worker (default: 1)') 277 | parser.add_argument('--output', help='Output file (default: stdout)') 278 | parser.add_argument('files', nargs='+', help='Parquet files to process') 279 | 280 | # Parse arguments 281 | args = parser.parse_args() 282 | 283 | # Calculate the actual number of workers to use (min of files and requested workers) 284 | num_chunks = (len(args.files) + args.chunk_size - 1) // args.chunk_size 285 | num_workers = min(args.workers, num_chunks) 286 | 287 | print(f"Starting with {num_workers} worker processes", file=sys.stderr) 288 | print(f"Processing {len(args.files)} files in chunks of {args.chunk_size}", file=sys.stderr) 289 | 290 | # Start the timer 291 | start_time = time.time() 292 | 293 | # Group files into chunks for better load balancing 294 | file_chunks = list(chunks(args.files, args.chunk_size)) 295 | 296 | # Create a pool of worker processes 297 | with mp.Pool(processes=num_workers, initializer=worker_init) as pool: 298 | # Create a partial function with fixed parameters 299 | process_func = partial(process_chunk) 300 | 301 | # Process chunks in parallel 302 | results = pool.map(process_func, file_chunks) 303 | 304 | # Merge results from all workers 305 | merged_freqtab = merge_worker_results(results) 306 | 307 | # Calculate elapsed time 308 | elapsed = time.time() - start_time 309 | print(f"Total processing time: {elapsed:.2f} seconds", file=sys.stderr) 310 | print(f"Processed {len(args.files)} files", file=sys.stderr) 311 | print(f"Found data for {len(merged_freqtab)} unique users", file=sys.stderr) 312 | 313 | # Write the results 314 | write_results_to_file(merged_freqtab, args.output) 315 | 316 | def process_chunk(file_paths): 317 | """Process a chunk of files and return combined frequency tables.""" 318 | chunk_results = {} 319 | 320 | for file_path in file_paths: 321 | if not os.path.exists(file_path): 322 | print(f"File not found: {file_path}", file=sys.stderr) 323 | continue 324 | 325 | file_results = process_parquet_file(file_path) 326 | 327 | # Merge this file's results into the chunk results 328 | for username, freqtab in file_results.items(): 329 | if username not in chunk_results: 330 | chunk_results[username] = freqtab 331 | else: 332 | chunk_results[username] = merge_frequency_tables( 333 | chunk_results[username], freqtab 334 | ) 335 | 336 | return chunk_results 337 | 338 | if __name__ == "__main__": 339 | main() 340 | -------------------------------------------------------------------------------- /gen-top-words.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import argparse 3 | import re 4 | import sys 5 | from collections import Counter 6 | from html import unescape 7 | from urllib.parse import urlparse 8 | 9 | import pyarrow.parquet as pq 10 | import pandas as pd 11 | from bs4 import BeautifulSoup 12 | 13 | def extract_domain(url): 14 | """Extract domain name from a URL.""" 15 | if not url: 16 | return None 17 | 18 | try: 19 | # Handle special cases like relative URLs or malformed URLs 20 | if not url.startswith(('http://', 'https://')): 21 | if url.startswith('//'): 22 | url = 'http:' + url 23 | else: 24 | url = 'http://' + url 25 | 26 | # Parse URL 27 | parsed_url = urlparse(url) 28 | 29 | # Get domain (remove www. if present) 30 | domain = parsed_url.netloc 31 | domain = re.sub(r'^www\.', '', domain) 32 | 33 | return domain 34 | except Exception: 35 | # If URL parsing fails, return the original string as fallback 36 | return url 37 | 38 | def clean_html(html_text): 39 | """Clean HTML from text, preserving paragraph structure and link information.""" 40 | if pd.isna(html_text): 41 | return None 42 | 43 | # If the text is not a string (somehow), convert it to string 44 | if not isinstance(html_text, str): 45 | return str(html_text) 46 | 47 | # Unescape HTML entities 48 | unescaped = unescape(html_text) 49 | 50 | # Only use BeautifulSoup if the text actually contains HTML-like patterns 51 | if re.search(r'<[a-zA-Z]+[^>]*>|&[a-zA-Z]+;', unescaped): 52 | # Use BeautifulSoup to parse and extract text 53 | soup = BeautifulSoup(unescaped, 'html.parser') 54 | 55 | # Replace

tags with newlines 56 | for p in soup.find_all('p'): 57 | p.replace_with('\n\n' + p.get_text() + '\n\n') 58 | 59 | # Extract text from links while preserving the URL 60 | for a in soup.find_all('a'): 61 | href = a.get('href', '') 62 | a.replace_with(f"{a.get_text()} [{href}]") 63 | 64 | cleaned_text = soup.get_text().strip() 65 | else: 66 | # If no HTML detected, just use the text as is 67 | cleaned_text = unescaped 68 | 69 | # Clean up extra whitespace 70 | cleaned_text = re.sub(r'\n{3,}', '\n\n', cleaned_text) 71 | 72 | return cleaned_text 73 | 74 | def extract_words(text): 75 | """Extract words and URL domains from text.""" 76 | if pd.isna(text) or not text: 77 | return [] 78 | 79 | words = [] 80 | 81 | # Find URLs and extract domains 82 | url_pattern = r'\[(https?://[^\s\]]+)\]|\b(https?://[^\s]+)\b' 83 | 84 | # Function to extract domains from URLs 85 | def extract_url_domain(match): 86 | url = match.group(1) or match.group(2) 87 | domain = extract_domain(url) 88 | if domain: 89 | words.append(f"linkto:{domain}") 90 | return " " # Replace URL with space 91 | 92 | # Replace URLs with their domain tokens and extract domains 93 | processed_text = re.sub(url_pattern, extract_url_domain, text) 94 | 95 | # Tokenize the text - split by whitespace and filter out punctuation 96 | # Keep apostrophes and hyphens within words 97 | extracted_words = re.findall(r'\b[a-zA-Z][a-zA-Z\'-]*[a-zA-Z]\b|\b[a-zA-Z]\b', processed_text.lower()) 98 | 99 | # Remove common stop words 100 | # stop_words = {'the', 'and', 'a', 'to', 'of', 'in', 'that', 'is', 'for', 'it', 'as', 'with', 'on', 'by', 'at', 'this', 'an', 'are', 'be', 'or', 'was', 'but', 'not', 'have', 'from', 'has', 'had', 'will', 'they', 'what', 'which', 'who', 'when', 'where', 'how', 'why', 'their', 'there', 'these', 'those', 'been', 'being', 'would', 'could', 'should', 'you', 'your', 'i', 'my', 'me', 'we', 'us', 'our'} 101 | stop_words = {} 102 | 103 | # Add words that pass the filter 104 | for word in extracted_words: 105 | if word not in stop_words and len(word) > 1: 106 | words.append(word) 107 | 108 | return words 109 | 110 | def process_parquet_file(file_path, word_counter): 111 | """Process a single parquet file and update global word counter.""" 112 | try: 113 | print(f"Processing file: {file_path}", file=sys.stderr) 114 | 115 | # Open the parquet file 116 | parquet_file = pq.ParquetFile(file_path) 117 | 118 | # Process the file in batches (row groups) 119 | for batch in parquet_file.iter_batches(batch_size=1000): 120 | # Convert batch to pandas DataFrame 121 | batch_df = batch.to_pandas() 122 | 123 | # Filter to only include comments 124 | comments_df = batch_df[batch_df['type'] == 'comment'].copy() 125 | 126 | # Skip if no comments in this batch 127 | if comments_df.empty: 128 | continue 129 | 130 | # Process each comment 131 | for index, row in comments_df.iterrows(): 132 | # Skip if no text 133 | if pd.isna(row['text']): 134 | continue 135 | 136 | # Clean the text 137 | cleaned_text = clean_html(row['text']) 138 | 139 | # Extract words 140 | words = extract_words(cleaned_text) 141 | 142 | # Update word counter 143 | word_counter.update(words) 144 | 145 | print(f"Finished processing file: {file_path}", file=sys.stderr) 146 | return True 147 | 148 | except Exception as e: 149 | print(f"Error processing file '{file_path}': {e}", file=sys.stderr) 150 | return False 151 | 152 | def main(): 153 | # Set up argument parser 154 | parser = argparse.ArgumentParser(description='Generate a list of the most frequent words from parquet files.') 155 | parser.add_argument('files', nargs='+', help='Parquet files to process') 156 | parser.add_argument('--count', type=int, default=10000, help='Number of top words to output') 157 | parser.add_argument('--output-file', required=True, help='File to write the top words') 158 | 159 | # Parse arguments 160 | args = parser.parse_args() 161 | 162 | # Initialize counter for words 163 | word_counter = Counter() 164 | 165 | # Count of successfully processed files 166 | processed_count = 0 167 | 168 | # Process each parquet file 169 | for file_path in args.files: 170 | try: 171 | if process_parquet_file(file_path, word_counter): 172 | processed_count += 1 173 | except FileNotFoundError: 174 | print(f"Error: The file '{file_path}' was not found. Skipping.", file=sys.stderr) 175 | except Exception as e: 176 | print(f"An error occurred processing file '{file_path}': {e}. Skipping.", file=sys.stderr) 177 | 178 | # Report processing summary 179 | print(f"Successfully processed {processed_count} of {len(args.files)} files.", file=sys.stderr) 180 | print(f"Found {len(word_counter)} unique words.", file=sys.stderr) 181 | 182 | # Get the top N words 183 | top_words = [word for word, count in word_counter.most_common(args.count)] 184 | 185 | # Write to output file 186 | with open(args.output_file, 'w', encoding='utf-8') as f: 187 | for word in top_words: 188 | f.write(f"{word}\n") 189 | 190 | print(f"Wrote top {len(top_words)} words to {args.output_file}", file=sys.stderr) 191 | 192 | if __name__ == "__main__": 193 | main() 194 | -------------------------------------------------------------------------------- /insert.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import json 3 | import redis 4 | import numpy as np 5 | import sys 6 | import argparse 7 | from typing import Dict, List, Tuple, Set 8 | from collections import defaultdict 9 | 10 | # Global dictionary to map words to vector indices 11 | word_to_index = {} 12 | 13 | # Global dictionaries for word statistics 14 | word_counts = defaultdict(int) # How many users use this word 15 | word_freq_sum = defaultdict(float) # Sum of frequencies for calculating mean 16 | word_freq_sq_sum = defaultdict(float) # Sum of squared frequencies for variance 17 | total_users = 0 # Total number of users processed 18 | 19 | def parse_args(): 20 | """Parse command line arguments""" 21 | parser = argparse.ArgumentParser(description='Process user frequency tables and store them as vectors in Redis') 22 | 23 | # Required arguments 24 | parser.add_argument('filename', help='JSONL file containing user frequency tables') 25 | parser.add_argument('--top-words-file', required=True, help='File containing top words, one per line') 26 | parser.add_argument('--top-words-count', type=int, required=True, help='Number of top words to use') 27 | 28 | # Optional arguments with defaults 29 | parser.add_argument('--host', default='localhost', help='Redis host (default: localhost)') 30 | parser.add_argument('--port', type=int, default=6379, help='Redis port (default: 6379)') 31 | parser.add_argument('--keyname', default='hn_fingerprints', help='Redis key to store vectors (default: hn_fingerprints)') 32 | parser.add_argument('--suffix', default='', help='String to append to usernames, useful to add the same users with different names and check if the style detection works (default: empty string, nothing added)') 33 | 34 | return parser.parse_args() 35 | 36 | def load_top_words(filename: str, count: int) -> List[str]: 37 | """ 38 | Load the top words from a file, limited by count 39 | Returns a list of top words (ordered to maintain consistent vector indices) 40 | """ 41 | global word_to_index 42 | 43 | top_words = [] 44 | try: 45 | with open(filename, 'r') as f: 46 | for i, line in enumerate(f): 47 | if i >= count: 48 | break 49 | word = line.strip() 50 | if word: 51 | top_words.append(word) 52 | # Create mapping from word to its index in the vector 53 | word_to_index[word] = i 54 | 55 | print(f"Loaded {len(top_words)} top words from {filename}") 56 | return top_words 57 | except Exception as e: 58 | print(f"Error loading top words file: {e}") 59 | sys.exit(1) 60 | 61 | def parse_frequency_table(json_data: str) -> Tuple[str, Dict[str, int]]: 62 | """ 63 | Parse the frequency table from JSON data 64 | Returns the username and a dictionary of word frequencies 65 | """ 66 | try: 67 | data = json.loads(json_data) 68 | username = data.get("by", "unknown") 69 | freq_table = data.get("freqtab", {}) 70 | return username, freq_table 71 | except json.JSONDecodeError as e: 72 | print(f"JSON decoding error: {e}") 73 | print(f"Problematic data: {json_data[:100]}...") 74 | raise 75 | 76 | def calculate_global_statistics(filename: str, top_words: List[str]): 77 | """ 78 | First pass: Calculate global statistics (mean, stddev) for each word 79 | """ 80 | global word_counts, word_freq_sum, word_freq_sq_sum, total_users 81 | 82 | print("First pass: Calculating global word statistics...") 83 | 84 | try: 85 | with open(filename, 'r') as f: 86 | for line_num, line in enumerate(f, 1): 87 | line = line.strip() 88 | if not line: # Skip empty lines 89 | continue 90 | 91 | try: 92 | _, freq_table = parse_frequency_table(line) 93 | 94 | # Calculate total words in this document for relative frequencies 95 | total_words = sum(freq_table.values()) 96 | if total_words == 0: 97 | continue 98 | 99 | # Update statistics for words in top_words 100 | for word, count in freq_table.items(): 101 | if word in top_words: 102 | # Convert to relative frequency (percentage) 103 | rel_freq = count / total_words 104 | 105 | # Update statistics 106 | word_counts[word] += 1 107 | word_freq_sum[word] += rel_freq 108 | word_freq_sq_sum[word] += rel_freq * rel_freq 109 | 110 | total_users += 1 111 | 112 | if line_num % 1000 == 0: 113 | print(f"Processed {line_num} users for statistics...") 114 | 115 | except Exception as e: 116 | print(f"Error processing line {line_num} for statistics: {e}") 117 | continue 118 | 119 | print(f"Completed statistics calculation for {total_users} users") 120 | 121 | # Calculate means and standard deviations 122 | word_means = {} 123 | word_stddevs = {} 124 | 125 | for word in top_words: 126 | if word_counts[word] > 1: # Need at least 2 occurrences for stddev 127 | mean = word_freq_sum[word] / total_users 128 | # Calculate variance using the computational formula: Var(X) = E(X²) - E(X)² 129 | variance = (word_freq_sq_sum[word] / total_users) - (mean * mean) 130 | # Use Bessel's correction for sample standard deviation. 131 | # You may wonder why to use Bessel's correction if we have all 132 | # the HN users. Well, the code assumes that this is still a 133 | # sample of a much larger theoretical population. 134 | corrected_variance = variance * total_users / (total_users - 1) if total_users > 1 else variance 135 | stddev = max(np.sqrt(corrected_variance), 1e-6) # Avoid division by zero 136 | 137 | word_means[word] = mean 138 | word_stddevs[word] = stddev 139 | else: 140 | # If a word doesn't occur enough, use fallback values 141 | word_means[word] = 0.0 142 | word_stddevs[word] = 1.0 # Avoid division by zero 143 | 144 | print(f"Calculated statistics for {len(word_means)} words") 145 | return word_means, word_stddevs 146 | 147 | except Exception as e: 148 | print(f"Error calculating global statistics: {e}") 149 | import traceback 150 | traceback.print_exc() 151 | sys.exit(1) 152 | 153 | def create_user_vector(freq_table: Dict[str, int], top_words: List[str], 154 | word_means: Dict[str, float], word_stddevs: Dict[str, float]) -> Tuple[np.ndarray, int]: 155 | """ 156 | Create a user vector based on the frequency table using Burrows' Delta 157 | standardization. Returns the vector and the total word count. 158 | """ 159 | # Initialize a zero vector with dimension equal to the number of top words 160 | vector = np.zeros(len(top_words), dtype=np.float32) 161 | 162 | # Calculate total words for relative frequencies and for metadata 163 | total_words = sum(freq_table.values()) 164 | if total_words == 0: 165 | return vector, 0 166 | 167 | # Count words that are in our top words list (for metadata) 168 | top_words_count = 0 169 | 170 | # Process each word in the frequency table, but only if it's in top_words 171 | for word, frequency in freq_table.items(): 172 | # Skip invalid words or frequency values 173 | if not isinstance(word, str) or not word: 174 | continue 175 | if not isinstance(frequency, (int, float)) or frequency <= 0: 176 | continue 177 | 178 | # Skip words not in our top words list 179 | if word not in word_to_index: 180 | continue 181 | 182 | # Add to the count of words in our list 183 | top_words_count += frequency 184 | 185 | # Convert to relative frequency 186 | rel_freq = frequency / total_words 187 | 188 | # Standardize using z-score: z = (freq - mean) / stddev 189 | mean = word_means.get(word, 0.0) 190 | stddev = word_stddevs.get(word, 1.0) # Default to 1.0 to avoid division by zero 191 | 192 | z_score = (rel_freq - mean) / stddev 193 | 194 | # Set the z-score directly in the vector at the word's index 195 | vector[word_to_index[word]] = z_score 196 | 197 | return vector, top_words_count 198 | 199 | def add_user_to_redis(r: redis.Redis, username: str, vector: np.ndarray, word_count: int, redis_key: str, suffix: str) -> bool: 200 | """ 201 | Add a user vector to Redis using VADD and set attributes with word count 202 | """ 203 | try: 204 | # Convert the numpy array to bytes in FP32 format 205 | vector_bytes = vector.astype(np.float32).tobytes() 206 | 207 | # Use VADD with FP32 to add the vector 208 | metadata = json.dumps({"wordcount": word_count}) 209 | result = r.execute_command( 210 | 'VADD', 211 | redis_key, 212 | 'FP32', 213 | vector_bytes, 214 | username+suffix, 215 | "SETATTR", 216 | metadata 217 | ) 218 | return result == 1 219 | except Exception as e: 220 | print(f"Error adding user {username} to Redis: {e}") 221 | return False 222 | 223 | def process_file(filename: str, redis_conn: redis.Redis, redis_key: str, top_words: List[str], word_means: Dict[str, float], word_stddevs: Dict[str, float], suffix: str) -> int: 224 | """ 225 | Process a file containing user frequency tables in JSONL format 226 | Returns the number of successful additions to Redis 227 | """ 228 | success_count = 0 229 | 230 | try: 231 | print(f"Second pass: Processing JSONL file to create vectors: {filename}") 232 | # Read the file line by line to handle JSONL format 233 | with open(filename, 'r') as f: 234 | for line_num, line in enumerate(f, 1): 235 | line = line.strip() 236 | if not line: # Skip empty lines 237 | continue 238 | 239 | try: 240 | # Parse the JSON object in this line 241 | username, freq_table = parse_frequency_table(line) 242 | 243 | # Count how many words are in the top words list 244 | common_words = set(freq_table.keys()).intersection(word_to_index.keys()) 245 | 246 | # Print some stats about the user 247 | word_count = len(freq_table) 248 | common_count = len(common_words) 249 | 250 | if line_num % 100 == 0: 251 | print(f"Line {line_num}: User '{username}' with {word_count} unique words ({common_count} in top words list)") 252 | 253 | # Create the vector fingerprint using Burrows' Delta standardization 254 | vector, top_words_count = create_user_vector(freq_table, top_words, word_means, word_stddevs) 255 | 256 | # Add to Redis with word count metadata 257 | if add_user_to_redis(redis_conn, username, vector, top_words_count, redis_key, suffix): 258 | success_count += 1 259 | if line_num % 100 == 0: 260 | print(f"Successfully added user: {username} (word count: {top_words_count})") 261 | else: 262 | print(f"Failed to add user: {username}") 263 | 264 | except json.JSONDecodeError as e: 265 | print(f"Error parsing JSON at line {line_num}: {e}") 266 | continue 267 | except Exception as e: 268 | print(f"Error processing user at line {line_num}: {e}") 269 | continue 270 | 271 | # Print progress periodically 272 | if line_num % 100 == 0: 273 | print(f"Processed {line_num} lines, added {success_count} users so far...") 274 | 275 | except Exception as e: 276 | print(f"Error processing file {filename}: {e}") 277 | import traceback 278 | traceback.print_exc() 279 | 280 | return success_count 281 | 282 | def main(): 283 | # Parse command line arguments 284 | args = parse_args() 285 | 286 | # Load top words from file 287 | top_words = load_top_words(args.top_words_file, args.top_words_count) 288 | 289 | if not top_words: 290 | print("No top words loaded, exiting.") 291 | sys.exit(1) 292 | 293 | print("=" * 50) 294 | print(f"User Fingerprinting with Redis Vector Sets (Burrows' Delta)") 295 | print("=" * 50) 296 | print(f"File: {args.filename}") 297 | print(f"Redis: {args.host}:{args.port}") 298 | print(f"Redis key: {args.keyname}") 299 | print(f"Top words file: {args.top_words_file}") 300 | print(f"Top words count: {args.top_words_count} (loaded {len(top_words)})") 301 | print(f"Vector dimension: {len(top_words)}") 302 | print("=" * 50) 303 | 304 | # Connect to Redis 305 | try: 306 | r = redis.Redis(host=args.host, port=args.port, decode_responses=False) 307 | r.ping() # Check connection 308 | 309 | # Verify Vector Sets are available 310 | try: 311 | r.execute_command('VCARD', args.keyname) 312 | except redis.exceptions.ResponseError as e: 313 | # If key doesn't exist, that's fine 314 | if 'key does not exist' not in str(e).lower(): 315 | raise 316 | 317 | print("Connected to Redis successfully") 318 | 319 | except Exception as e: 320 | print(f"Error connecting to Redis: {e}") 321 | print("Make sure Redis is running and the Vector Sets module is loaded") 322 | sys.exit(1) 323 | 324 | # FIRST PASS: Calculate global statistics 325 | word_means, word_stddevs = calculate_global_statistics(args.filename, top_words) 326 | 327 | # SECOND PASS: Process the file and create vectors 328 | success_count = process_file(args.filename, r, args.keyname, top_words, word_means, word_stddevs, args.suffix) 329 | 330 | if success_count > 0: 331 | print("\nSummary:") 332 | print(f"Successfully added {success_count} users to Redis Vector Set") 333 | print(f"Total words in the vector space: {len(word_to_index)}") 334 | 335 | # Print the total number of vectors in the set 336 | try: 337 | total = r.execute_command('VCARD', args.keyname) 338 | print(f"Total vectors in {args.keyname}: {total}") 339 | 340 | # Example of how to use VSIM with FILTER 341 | print("\nExample search with word count filter:") 342 | print("To find similar users with at least 1000 words:") 343 | print(f"VSIM {args.keyname} 20 FILTER \".wordcount < 5000\"") 344 | except Exception as e: 345 | print(f"Unable to get total vector count: {e}") 346 | else: 347 | print("\nNo users were added to the Redis Vector Set") 348 | 349 | print("\nDone!") 350 | 351 | if __name__ == "__main__": 352 | try: 353 | main() 354 | except KeyboardInterrupt: 355 | print("\nProcess interrupted by user") 356 | except Exception as e: 357 | print(f"\nUnexpected error: {e}") 358 | import traceback 359 | traceback.print_exc() 360 | -------------------------------------------------------------------------------- /top.txt: -------------------------------------------------------------------------------- 1 | the 2 | to 3 | and 4 | of 5 | is 6 | that 7 | in 8 | it 9 | you 10 | for 11 | be 12 | this 13 | on 14 | not 15 | are 16 | but 17 | with 18 | have 19 | as 20 | if 21 | they 22 | or 23 | can 24 | was 25 | at 26 | an 27 | it's 28 | like 29 | so 30 | just 31 | more 32 | from 33 | people 34 | your 35 | my 36 | what 37 | would 38 | do 39 | about 40 | all 41 | there 42 | by 43 | we 44 | one 45 | their 46 | don't 47 | some 48 | will 49 | think 50 | has 51 | than 52 | how 53 | when 54 | which 55 | no 56 | get 57 | out 58 | because 59 | them 60 | up 61 | me 62 | time 63 | even 64 | other 65 | also 66 | use 67 | i'm 68 | only 69 | much 70 | any 71 | who 72 | then 73 | good 74 | work 75 | really 76 | make 77 | way 78 | very 79 | could 80 | most 81 | know 82 | something 83 | been 84 | want 85 | had 86 | see 87 | into 88 | need 89 | should 90 | things 91 | were 92 | being 93 | that's 94 | where 95 | why 96 | now 97 | same 98 | still 99 | many 100 | these 101 | well 102 | he 103 | those 104 | lot 105 | does 106 | better 107 | years 108 | too 109 | doesn't 110 | point 111 | you're 112 | new 113 | here 114 | us 115 | using 116 | going 117 | actually 118 | thing 119 | over 120 | go 121 | say 122 | its 123 | right 124 | i've 125 | code 126 | problem 127 | used 128 | first 129 | data 130 | might 131 | sure 132 | different 133 | without 134 | our 135 | probably 136 | money 137 | can't 138 | find 139 | seems 140 | take 141 | never 142 | though 143 | while 144 | every 145 | did 146 | own 147 | isn't 148 | someone 149 | there's 150 | few 151 | his 152 | doing 153 | enough 154 | system 155 | after 156 | back 157 | pretty 158 | etc 159 | less 160 | such 161 | great 162 | company 163 | maybe 164 | software 165 | may 166 | before 167 | google 168 | around 169 | anything 170 | long 171 | always 172 | since 173 | article 174 | down 175 | off 176 | two 177 | case 178 | read 179 | example 180 | idea 181 | made 182 | having 183 | makes 184 | least 185 | already 186 | am 187 | bad 188 | world 189 | through 190 | part 191 | look 192 | another 193 | both 194 | didn't 195 | hard 196 | business 197 | got 198 | big 199 | real 200 | i'd 201 | companies 202 | yes 203 | day 204 | they're 205 | free 206 | working 207 | bit 208 | between 209 | best 210 | mean 211 | language 212 | far 213 | said 214 | making 215 | getting 216 | interesting 217 | experience 218 | able 219 | instead 220 | reason 221 | stuff 222 | year 223 | done 224 | end 225 | kind 226 | pay 227 | change 228 | little 229 | trying 230 | run 231 | try 232 | users 233 | rather 234 | market 235 | often 236 | understand 237 | web 238 | either 239 | anyone 240 | life 241 | give 242 | quite 243 | job 244 | small 245 | fact 246 | open 247 | keep 248 | start 249 | else 250 | app 251 | each 252 | nothing 253 | everything 254 | person 255 | everyone 256 | true 257 | wrong 258 | means 259 | support 260 | number 261 | last 262 | put 263 | high 264 | course 265 | possible 266 | feel 267 | works 268 | source 269 | site 270 | value 271 | once 272 | ever 273 | help 274 | come 275 | agree 276 | power 277 | product 278 | based 279 | apple 280 | easy 281 | large 282 | sense 283 | similar 284 | cost 285 | question 286 | thought 287 | believe 288 | likely 289 | old 290 | place 291 | user 292 | yet 293 | ago 294 | looking 295 | government 296 | almost 297 | away 298 | post 299 | build 300 | seem 301 | project 302 | whole 303 | issue 304 | saying 305 | set 306 | write 307 | times 308 | problems 309 | again 310 | 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braking 9792 | marking 9793 | proliferation 9794 | inefficiency 9795 | solvable 9796 | bitch 9797 | shove 9798 | adapting 9799 | asserting 9800 | spray 9801 | courage 9802 | macbooks 9803 | non-standard 9804 | shirts 9805 | complement 9806 | insisted 9807 | firebug 9808 | bracket 9809 | repercussions 9810 | bro 9811 | compounds 9812 | rust's 9813 | locate 9814 | causality 9815 | cabinet 9816 | mankind 9817 | disputes 9818 | dod 9819 | assemble 9820 | dhh 9821 | crawling 9822 | shoe 9823 | dimensional 9824 | parentheses 9825 | intrusive 9826 | localized 9827 | warp 9828 | securing 9829 | disproportionate 9830 | ambient 9831 | deviation 9832 | frontier 9833 | withdraw 9834 | argentina 9835 | supplements 9836 | legend 9837 | originated 9838 | div 9839 | downhill 9840 | heritage 9841 | internationally 9842 | insurers 9843 | breaches 9844 | admits 9845 | robotic 9846 | appointment 9847 | extinct 9848 | well-defined 9849 | cursory 9850 | misery 9851 | potato 9852 | breadth 9853 | adaptation 9854 | linkto:facebook.com 9855 | mismatch 9856 | verifiable 9857 | flagship 9858 | downmodded 9859 | awarded 9860 | contradicts 9861 | arbitrage 9862 | cartoon 9863 | fined 9864 | parent's 9865 | parsers 9866 | dividend 9867 | basing 9868 | knocking 9869 | yoga 9870 | appliance 9871 | pharmaceutical 9872 | normalized 9873 | shout 9874 | nuances 9875 | assistants 9876 | clickable 9877 | inexperienced 9878 | unproductive 9879 | grabbing 9880 | exceedingly 9881 | blend 9882 | entrance 9883 | lang 9884 | redirects 9885 | nutrients 9886 | laravel 9887 | accomplishment 9888 | plugging 9889 | nurse 9890 | confidently 9891 | linkto:washingtonpost.com 9892 | hn's 9893 | jetbrains 9894 | ecc 9895 | contextual 9896 | ramen 9897 | predictor 9898 | women's 9899 | patented 9900 | cigarette 9901 | firearms 9902 | remarks 9903 | warranted 9904 | hoc 9905 | barring 9906 | disinformation 9907 | fingerprinting 9908 | hetzner 9909 | semiconductor 9910 | altered 9911 | purposefully 9912 | filesystems 9913 | driver's 9914 | sticker 9915 | appstore 9916 | webapps 9917 | endlessly 9918 | gallery 9919 | jets 9920 | stretching 9921 | replaceable 9922 | swapped 9923 | unicorn 9924 | loudly 9925 | detached 9926 | flew 9927 | bargaining 9928 | investigations 9929 | legislature 9930 | beautifully 9931 | holocaust 9932 | read-only 9933 | addicts 9934 | brian 9935 | outsiders 9936 | sucking 9937 | jose 9938 | reactive 9939 | adjustment 9940 | hyperbolic 9941 | hills 9942 | emission 9943 | technologically 9944 | pre-existing 9945 | governmental 9946 | grandma 9947 | precedence 9948 | edu 9949 | freak 9950 | genome 9951 | linkto:gist.github.com 9952 | linkto:hn.algolia.com 9953 | dragged 9954 | buckets 9955 | dire 9956 | dedicate 9957 | hadoop 9958 | overweight 9959 | sap 9960 | reasoned 9961 | blade 9962 | linkto:tinyurl.com 9963 | activated 9964 | questioned 9965 | starter 9966 | manuals 9967 | metaprogramming 9968 | achievable 9969 | paranoia 9970 | tiling 9971 | sexism 9972 | suvs 9973 | employing 9974 | who'd 9975 | intact 9976 | modeled 9977 | harming 9978 | exporting 9979 | cruft 9980 | overlay 9981 | backbone 9982 | mathematica 9983 | ingredient 9984 | crux 9985 | cosmic 9986 | skimmed 9987 | pos 9988 | occupy 9989 | disprove 9990 | pv 9991 | overloaded 9992 | surveys 9993 | romantic 9994 | lacked 9995 | bureaucrats 9996 | couchdb 9997 | minimalist 9998 | scooter 9999 | accomplishments 10000 | possess 10001 | -------------------------------------------------------------------------------- /vshow.c: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | 8 | // Base64 encoding table 9 | static const char base64_table[] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"; 10 | 11 | // Function to encode data to base64 12 | size_t base64_encode(const unsigned char *data, size_t input_length, char *encoded_data) { 13 | size_t output_length = 4 * ((input_length + 2) / 3); 14 | size_t i, j; 15 | for (i = 0, j = 0; i < input_length;) { 16 | uint32_t octet_a = i < input_length ? data[i++] : 0; 17 | uint32_t octet_b = i < input_length ? data[i++] : 0; 18 | uint32_t octet_c = i < input_length ? data[i++] : 0; 19 | uint32_t triple = (octet_a << 16) + (octet_b << 8) + octet_c; 20 | encoded_data[j++] = base64_table[(triple >> 18) & 0x3F]; 21 | encoded_data[j++] = base64_table[(triple >> 12) & 0x3F]; 22 | encoded_data[j++] = base64_table[(triple >> 6) & 0x3F]; 23 | encoded_data[j++] = base64_table[triple & 0x3F]; 24 | } 25 | // Add padding if needed 26 | size_t mod_table[] = {0, 2, 1}; 27 | for (i = 0; i < mod_table[input_length % 3]; i++) 28 | encoded_data[output_length - 1 - i] = '='; 29 | return output_length; 30 | } 31 | 32 | int main() { 33 | // Initialize random seed for image ID 34 | srand(time(NULL)); 35 | 36 | // Read vector from stdin 37 | double *vector = NULL; 38 | size_t vector_size = 0; 39 | size_t vector_capacity = 0; 40 | char line[1024]; 41 | 42 | // Read one float per line from stdin 43 | while (fgets(line, sizeof(line), stdin) != NULL) { 44 | // Resize if needed 45 | if (vector_size >= vector_capacity) { 46 | vector_capacity = vector_capacity ? vector_capacity * 2 : 8; 47 | double *new_vector = realloc(vector, vector_capacity * sizeof(double)); 48 | if (!new_vector) { 49 | fprintf(stderr, "Memory allocation failed\n"); 50 | free(vector); 51 | return 1; 52 | } 53 | vector = new_vector; 54 | } 55 | 56 | // Convert to double and add to vector 57 | vector[vector_size++] = atof(line); 58 | } 59 | 60 | // Check if we got any values 61 | if (vector_size == 0) { 62 | fprintf(stderr, "No valid vector values found\n"); 63 | free(vector); 64 | return 1; 65 | } 66 | 67 | // Find max absolute value for normalization 68 | double max_abs_val = 0.0; 69 | for (size_t i = 0; i < vector_size; i++) { 70 | double abs_val = fabs(vector[i]); 71 | if (abs_val > max_abs_val) { 72 | max_abs_val = abs_val; 73 | } 74 | } 75 | 76 | // Normalize the vector 77 | if (max_abs_val > 0.0) { 78 | for (size_t i = 0; i < vector_size; i++) { 79 | vector[i] /= max_abs_val; 80 | } 81 | } 82 | 83 | // Calculate square dimension 84 | size_t square_dim = 1; 85 | while (square_dim * square_dim < vector_size) { 86 | square_dim++; 87 | } 88 | 89 | // Add 2 pixels total for border (1 on each side) 90 | size_t image_dim = square_dim + 2; 91 | 92 | // Create the bitmap (RGB) 93 | unsigned char *bitmap = malloc(image_dim * image_dim * 3); 94 | if (!bitmap) { 95 | fprintf(stderr, "Memory allocation failed\n"); 96 | free(vector); 97 | return 1; 98 | } 99 | 100 | // Fill with border color (gray #999999) 101 | memset(bitmap, 0x99, image_dim * image_dim * 3); 102 | 103 | // Fill the inner square with vector components 104 | for (size_t y = 0; y < square_dim; y++) { 105 | for (size_t x = 0; x < square_dim; x++) { 106 | size_t idx = y * square_dim + x; 107 | size_t pos = ((y + 1) * image_dim + (x + 1)) * 3; // Offset by 1 for border 108 | 109 | if (idx < vector_size) { 110 | double val = vector[idx]; 111 | if (val > 0) { 112 | // Positive: Red (scaling from black to full red) 113 | bitmap[pos] = (unsigned char)(val * 255); // R 114 | bitmap[pos + 1] = 0; // G 115 | bitmap[pos + 2] = 0; // B 116 | } else if (val < 0) { 117 | // Negative: Green (scaling from black to full green) 118 | bitmap[pos] = 0; // R 119 | bitmap[pos + 1] = (unsigned char)(-val * 255); // G 120 | bitmap[pos + 2] = 0; // B 121 | } else { 122 | // Zero: Black 123 | bitmap[pos] = 0; // R 124 | bitmap[pos + 1] = 0; // G 125 | bitmap[pos + 2] = 0; // B 126 | } 127 | } else { 128 | // Fill any unused cells with black 129 | bitmap[pos] = 0; // R 130 | bitmap[pos + 1] = 0; // G 131 | bitmap[pos + 2] = 0; // B 132 | } 133 | } 134 | } 135 | 136 | // Setup base64 encoding 137 | size_t bitmap_size = image_dim * image_dim * 3; 138 | size_t encoded_size = 4 * ((bitmap_size + 2) / 3); 139 | char *encoded_data = malloc(encoded_size + 1); 140 | if (!encoded_data) { 141 | fprintf(stderr, "Memory allocation failed\n"); 142 | free(vector); 143 | free(bitmap); 144 | return 1; 145 | } 146 | 147 | // Random ID for the image 148 | long id = rand(); 149 | 150 | // Encode and display 151 | base64_encode(bitmap, bitmap_size, encoded_data); 152 | encoded_data[encoded_size] = '\0'; 153 | 154 | printf("\033_Ga=T,i=%lu,f=24,s=%zu,v=%zu,c=20,r=10,q=2;", id, image_dim, image_dim); 155 | printf("%s", encoded_data); 156 | printf("\033\\"); 157 | printf("\n"); 158 | fflush(stdout); 159 | 160 | // Clean up 161 | free(encoded_data); 162 | free(bitmap); 163 | free(vector); 164 | 165 | return 0; 166 | } 167 | --------------------------------------------------------------------------------