diff --git a/.gitignore b/.gitignore index f85fe70..18d13df 100644 Binary files a/.gitignore and b/.gitignore differ diff --git a/games/Rock-Paper-Scissor/Rock-Paper-Scissor.py b/games/Rock-Paper-Scissor/Rock-Paper-Scissor.py index c21f797..4ef3148 100644 --- a/games/Rock-Paper-Scissor/Rock-Paper-Scissor.py +++ b/games/Rock-Paper-Scissor/Rock-Paper-Scissor.py @@ -5,27 +5,123 @@ def __init__(self): """Initializes the game state without blocking execution.""" self.user_score = 0 self.computer_score = 0 - self.rounds_played = 0 + self.rounds_played = 0 # Perfectly mirrors the JS choices array ['rock', 'paper', 'scissors'] self.choices = ["rock", "paper", "scissors"] + # Counter look-up: what beats each move + self.beaten_by = {"rock": "paper", "paper": "scissors", "scissors": "rock"} + # Player history for adaptive AI (capped at recent 20 moves) + self.player_history = [] + self.HISTORY_CAP = 20 + self.MIN_ADAPTIVE = 3 # rounds before adaptive mode activates + self.ADAPT_RATE = 0.70 # probability of playing counter vs random + # ── Adaptive AI logic ──────────────────────────────────────────────── + def _get_move_frequencies(self): + """Returns overall frequency dict for player history.""" + freq = {"rock": 0, "paper": 0, "scissors": 0} + for move in self.player_history: + freq[move] += 1 + return freq + + def _get_markov_transitions(self, last_move): + """Returns transition counts from last_move to the next move in history.""" + transitions = {"rock": 0, "paper": 0, "scissors": 0} + for i in range(len(self.player_history) - 1): + if self.player_history[i] == last_move: + transitions[self.player_history[i + 1]] += 1 + return transitions + + def _predict_player_move(self): + """ + Uses a blended Markov-chain + frequency model to predict the player's + next move. Returns (predicted_move, confidence_pct) or (None, None) + if there is not enough data. + """ + n = len(self.player_history) + if n < self.MIN_ADAPTIVE: + return None, None + + freq = self._get_move_frequencies() + last = self.player_history[-1] + trans = self._get_markov_transitions(last) + total = sum(trans.values()) + + if total > 0: + # Blend: 60 % Markov, 40 % frequency + blended = {} + for c in self.choices: + blended[c] = (0.6 * trans[c] / total) + (0.4 * freq[c] / n) + predicted = max(blended, key=blended.get) + confidence = round(blended[predicted] * 100) + else: + # Fallback to pure frequency + predicted = max(freq, key=freq.get) + confidence = round(freq[predicted] / n * 100) + + return predicted, confidence + + def get_adaptive_computer_choice(self): + """ + Returns (computer_choice, predicted_player_move, confidence_pct, mode). + Plays the counter-move 70 % of the time; random otherwise for balance. + """ + predicted, confidence = self._predict_player_move() + + if predicted is None: + return random.choice(self.choices), None, None, "learning" + + if random.random() < self.ADAPT_RATE: + computer_choice = self.beaten_by[predicted] + mode = "adaptive" + else: + computer_choice = random.choice(self.choices) + mode = "random" + + return computer_choice, predicted, confidence, mode + + # ── Stats helpers ──────────────────────────────────────────────────── + def _most_frequent_choice(self): + if not self.player_history: + return None + freq = self._get_move_frequencies() + return max(freq, key=freq.get) + + # ── Gameplay ───────────────────────────────────────────────────────── def users_play(self): - """Handles a single round of interaction.""" + """Handles a single round of interaction with adaptive computer logic.""" user_choice = "" while user_choice not in self.choices: user_choice = input("Enter your choice (rock, paper, or scissors): ").lower() if user_choice not in self.choices: print("Invalid choice. Please choose rock, paper, or scissors.") - computer_choice = random.choice(self.choices) - print(f"Computer chose: {computer_choice}") + # AI decides BEFORE we record the player's move (prediction is based on prior history) + computer_choice, predicted, confidence, mode = self.get_adaptive_computer_choice() + # Print AI brain info after first MIN_ADAPTIVE rounds + if len(self.player_history) >= self.MIN_ADAPTIVE and predicted is not None: + fav = self._most_frequent_choice() + print(f"\n 🧠 Computer Brain [{mode.upper()}]") + print(f" Your favourite move : {fav}") + print(f" Predicted your move : {predicted} ({confidence}% confidence)") + print(f" Computer chose : {computer_choice}") + else: + remaining = self.MIN_ADAPTIVE - len(self.player_history) + if remaining > 0: + print(f"\n 🧠 Computer Brain [LEARNING] — observing for {remaining} more move(s)...") + print(f" Computer chose: {computer_choice}") + + # Record player move after AI has decided + self.player_history.append(user_choice) + if len(self.player_history) > self.HISTORY_CAP: + self.player_history.pop(0) + + # Determine round winner if user_choice == computer_choice: print("It's a Tie! 🤝") return "tie" - elif (user_choice == "rock" and computer_choice == "scissors") or \ - (user_choice == "paper" and computer_choice == "rock") or \ - (user_choice == "scissors" and computer_choice == "paper"): + elif self.beaten_by[computer_choice] == user_choice: print("You Win this round! 🎉") return "user" else: @@ -35,16 +131,24 @@ def users_play(self): def statistics(self): """Displays performance statistics matching the web dashboard metrics.""" print("\n--- Game Statistics ---") - print(f"Rounds Played: {self.rounds_played}") - print(f"Your Score: {self.user_score}") - print(f"Computer Score: {self.computer_score}") + print(f"Rounds Played : {self.rounds_played}") + print(f"Your Score : {self.user_score}") + print(f"Computer Score : {self.computer_score}") + fav = self._most_frequent_choice() + if fav: + freq = self._get_move_frequencies() + pct = round(freq[fav] / len(self.player_history) * 100) + print(f"Your Favourite : {fav} ({pct}% of plays)") def save_game(self): """Appends the final game results to a local tracking log.""" name = input("Enter your name to save the results (optional): ") if not name: name = "Anonymous" - result_string = f"Player: {name}, Final Score: {self.user_score} - {self.computer_score} (User-Computer), Rounds: {self.rounds_played}\n" + result_string = ( + f"Player: {name}, Final Score: {self.user_score} - {self.computer_score} " + f"(User-Computer), Rounds: {self.rounds_played}\n" + ) try: with open("game_results.txt", "a") as f: f.write(result_string) @@ -52,13 +156,14 @@ def save_game(self): except IOError: print("Error: Could not save game results to file.") - def play_game(self): + def play_game(self): """Launches the primary interactive gameplay loop.""" print("Welcome to Rock, Paper, Scissors!") + print("The computer will learn your patterns and adapt — good luck! 🧠") while True: self.rounds_played += 1 print(f"\n--- Round {self.rounds_played} ---") - + round_winner = self.users_play() if round_winner == "user": @@ -74,6 +179,7 @@ def play_game(self): self.save_game() break + # Standard execution block ensuring clean instantiation if __name__ == "__main__": game = Rock_Paper_Scissors() diff --git a/web-app/index.html b/web-app/index.html index b605074..7fb4c5b 100644 --- a/web-app/index.html +++ b/web-app/index.html @@ -1097,6 +1097,13 @@ + +
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