Super League stats & predictions

Unveiling the Thrill of Zambia's Football Super League

Welcome to the ultimate destination for all things related to the Zambia Football Super League. This platform is your go-to source for the latest match updates, expert betting predictions, and in-depth analysis. Whether you're a die-hard fan or a casual observer, our content is designed to keep you informed and engaged with every twist and turn of the season.

With daily updates on fresh matches, you'll never miss a moment of the action. Our expert analysts provide insightful predictions that can enhance your betting strategy, ensuring you stay ahead of the game. Dive into our comprehensive coverage and experience the excitement of Zambia's premier football league like never before.

Match Highlights and Daily Updates

Our dedicated team ensures you receive the most up-to-date information on every matchday. From thrilling victories to unexpected upsets, we capture the essence of each game with vivid descriptions and key statistics. Stay tuned for:

• Daily match summaries
• Player performance reviews
• Key moments and highlights

With our real-time updates, you can follow your favorite teams and players as they battle it out on the pitch. Whether you're watching live or catching up later, our content keeps you in the loop.

Expert Betting Predictions

Betting on football can be both exciting and rewarding. Our team of seasoned experts provides detailed predictions to help you make informed decisions. Here's what you can expect:

• Daily betting tips based on thorough analysis
• Insights into team form and player fitness
• Statistical breakdowns to guide your bets

Our predictions are crafted with precision, taking into account historical data, current trends, and expert intuition. Whether you're a seasoned bettor or new to the game, our insights can give you an edge.

In-Depth Team Analysis

Understanding the dynamics of each team is crucial for predicting match outcomes. Our in-depth analysis covers:

• Team formations and strategies
• Head-to-head records
• Key players and their impact on the game

We delve into the strengths and weaknesses of each squad, providing a comprehensive overview that helps you understand what to expect in upcoming matches.

Player Spotlights

Meet the stars of Zambia's Football Super League. Our player spotlights feature:

• Detailed profiles of top performers
• Interviews and behind-the-scenes insights
• Analysis of playing styles and career highlights

Get to know the individuals who bring passion and skill to the field, and see how they contribute to their teams' successes.

Historical Context and Records

The Zambia Football Super League has a rich history filled with memorable moments. Explore:

• Past champions and their legacies
• Milestone matches that defined seasons
• Record-breaking performances by players and teams

Understanding the league's history adds depth to your appreciation of current events, highlighting how today's matches are part of a larger narrative.

Interactive Features for Fans

Engage with fellow fans through our interactive features:

• Fan polls on match outcomes and player performances
<|repo_name|>DavidMolinaFerrando/IA<|file_sep|>/TP3 - Adversarial Search/agents.py import sys from collections import defaultdict import math import random from utils import manhattanDistance from game import Directions from searchAgents import SearchAgent class ReflexAgent(SearchAgent): """ Reflex agent which chooses an action at each choice point by examining its alternatives via a state evaluation function. The code below is provided as a guide. You are welcome to change it. """ def getAction(self, gameState): """ getAction chooses among the best options according to the evaluation function. Just like in the previous project, getAction takes a GameState and returns one of {NORTH, SOUTH, WEST, EAST}, STAY_THERE. See the project description for more details. """ "*** YOUR CODE HERE ***" # Collect legal moves and successor states legalMoves = gameState.getLegalActions() # Choose one of the best actions scores = [self.evaluationFunction(gameState, action) for action in legalMoves] bestScore = max(scores) bestIndices = [index for index in range(len(scores)) if scores[index] == bestScore] chosenIndex = random.choice(bestIndices) # Pick randomly among the best "Add more of your code here if you want to" return legalMoves[chosenIndex] def evaluationFunction(self, currentGameState, action): """ The evaluation function takes in the current and proposed successor GameStates (pacman.py) and returns a number, where higher numbers are better. The code below extracts some useful information from the state, like the current score, Pacman's position etc. Use it as a guide or inspiration for your own evaluation function. CURRENTLY DOES NOT CONSIDER FOOD LEFT! CURRENTLY DOES NOT CONSIDER CAPSULES LEFT! # Useful information you can extract from a GameState (pacman.py) lookaheadGameState = currentGameState.generatePacmanSuccessor(action) newPos = lookaheadGameState.getPacmanPosition() newFood = lookaheadGameState.getFood() newGhostStates = lookaheadGameState.getGhostStates() newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates] # Computes distance between Pacman's position after taking an action, # with all food dots on map foodList = newFood.asList() closestFoodDistances = [] for food in foodList: closestFoodDistances.append(manhattanDistance(newPos, food)) closestGhostDistances = [] for ghostState in newGhostStates: ghostPos = ghostState.getPosition() closestGhostDistances.append(manhattanDistance(newPos, ghostPos)) if min(closestGhostDistances) <=1: return -9999999 if min(closestFoodDistances) ==0: return max(closestGhostDistances) return lookaheadGameState.getScore() + min(closestFoodDistances) - min(closestGhostDistances) """ # Generate successor state after taking an action successorGameState = currentGameState.generatePacmanSuccessor(action) newPos = successorGameState.getPacmanPosition() newFood = successorGameState.getFood() newGhostStates = successorGameState.getGhostStates() newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates] # Compute distances between Pacman's position after taking an action, # with all food dots on map foodList = newFood.asList() closestFoodDistances = [] for food in foodList: closestFoodDistances.append(manhattanDistance(newPos, food)) # Compute distances between Pacman's position after taking an action, # with all ghosts on map closestGhostDistances = [] for ghostState in newGhostStates: ghostPos = ghostState.getPosition() closestGhostDistances.append(manhattanDistance(newPos, ghostPos)) # If there is at least one ghost within one step away from Pacman after taking an action, # penalize that state if min(closestGhostDistances) <=1: return -9999999 # If there is at least one food dot within one step away from Pacman after taking an action, # prioritize that state if min(closestFoodDistances) ==0: return max(closestGhostDistances) # Otherwise compute score based on number of food dots left on map, # distance between Pacman's position after taking an action, # with nearest food dot on map, # distance between Pacman's position after taking an action, # with nearest ghost on map return successorGameState.getScore() + (len(foodList)/10.) + (min(closestFoodDistances)/10.) - min(closestGhostDistances) class MinimaxAgent(ReflexAgent): """ Your minimax agent (question 1) """ def getAction(self, gameState): """ Returns minimax action from current gameState using self.depth and self.evaluationFunction. Here are some method calls that might be useful when implementing minimax. gameState.getLegalActions(agentIndex): Returns a list of legal actions for an agent agentIndex=0 means Pacman, ghosts are >= 1 gameState.generateSuccessor(agentIndex, action): Returns the successor game state after an agent takes an action gameState.getNumAgents(): Returns the total number of agents in the game gameState.isWin(): Returns whether or not the game state is a winning state gameState.isLose(): Returns whether or not the game state is a losing state """ "*** YOUR CODE HERE ***" def minimax(gameState,currentDepth,currentAgentIndex,maxDepth): if(currentDepth == maxDepth or gameState.isWin() or gameState.isLose()): return self.evaluationFunction(gameState), None numAgents=gameState.getNumAgents() legalActions=gameState.getLegalActions(currentAgentIndex) if(currentAgentIndex==0): v=float('-inf') bestAction=None for action in legalActions: successor=minimax(gameState.generateSuccessor(currentAgentIndex,action),currentDepth+1,(currentAgentIndex+1)%numAgents,maxDepth)[0] if(successor>v): v=successor bestAction=action return v,bestAction else: v=float('inf') bestAction=None for action in legalActions: successor=minimax(gameState.generateSuccessor(currentAgentIndex,action),currentDepth,(currentAgentIndex+1)%numAgents,maxDepth)[0] if(successorv): v=successor bestAction=action alpha=max(alpha,v) if(beta<=alpha): break return v,bestAction else: v=float('inf') bestAction=None for action in legalActions: successor=alphabeta(gameState.generateSuccessor(currentAgentIndex,action),currentDepth,(currentAgentIndex+1)%numAgents,maxDepth,alpha,beta)[0] if(successorv): v=successor bestAction=action return v,bestAction else: probabilities=[1./len(legalActions)]*len(legalActions) values=[] for i in range(len(legalActions)): values.append(expectimax(gameState.generateSuccessor(currentAgentIndex,legalActions[i]),currentDepth,(currentAgentIndex+1)%numAgents,maxDepth)[0]) expValue=sum([values[i]*probabilities[i]for i in range(len(legalActions))]) return expValue,None value,_=expectimax(gameState,0,0,self.depth) return _ def betterEvaluationFunction(currentGameState): """ Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable evaluation function (question 5). DESCRIPTION: The function receives as input a GameState object that contains all relevant information about a given state of Pacman's environment. RETURNS: A numeric value that represents how good/bad this particular state is. NOTES: The closer this value is to positive infinity (float('inf')), better this state is considered; vice versa. ADVICE: The evaluation function should take into account all available info provided by GameState object. It should also consider both short-term gains (e.g., eating pellets/ghosts) as well as long-term consequences (e.g., getting caught by ghosts). It should also handle both regular mode (i.e., no scared ghosts) as well as frightened mode (i.e., scared ghosts). More specifically: - If Pacman eats all available food dots without being caught by any ghost during this process then it will get maximum points possible; therefore this scenario should be prioritized over any other scenario. - If there are no available food dots but there are still scared ghosts then Pacman should prioritize eating scared ghosts since this will provide him with short-term gain; however he should also make sure he doesn't get caught by any regular ghost while chasing scared ghosts. - If there are no available food dots but there are no scared ghosts then Pacman should prioritize moving away from any nearby regular ghosts since he won't get any points by eating them anyway. - If there are both available food dots AND scared ghosts then Pacman should prioritize eating scared ghosts since this will provide him with short-term gain; however he should also make sure he doesn't get caught by any regular ghost while chasing scared ghosts. - If there are both available food dots AND regular ghosts then Pacman should prioritize eating available food dots while making sure he doesn't get caught by any regular ghost; however he should also make sure he doesn't move too far away from nearest scared ghost since he might lose opportunity to eat him. """ "*** YOUR CODE HERE ***" pacmanPosition=currentGameState.getPacmanPosition() pelletList=currentGameState.getCapsules() numPellets=len(pelletList) pelletValues=[self.betterEvaluationFunctionForCapsules(currentGameState,capsule)for capsule in pelletList] numCapsules=len(pelletValues) maxPelletValue=max(pelletValues) minPelletValue=min(pelletValues) avgPelletValue=sum(pelletValues)/float(numCapsules) closestPelletDistance=min([self.betterEvaluationFunctionForCapsules(currentGameState,capsule)for capsule in pelletList]) def betterEvaluationFunctionForCapsules(self,currentGameState,capsule): pacmanPosition=currentGameState.getPacmanPosition() capsules=currentGameState.getCapsules() ghostPositions=[ghost.getPosition()for ghost in currentGameState.getGhostStates()] distToClosestCapsule=min([self.manhattanDistance(pacmanPosition,capsule)for capsule in capsules]) distToClosestCapsuleWithGhosts=[self.manhattanDistance(capsule,gPosition)+self.manhattanDistance(pacmanPosition,capsule)for gPosition in ghostPositions] distToClosestCapsuleWithGhosts.append(distToClosestCapsule) distToClosestCapsuleWithGhosts.sort() distToClosestCapsuleWithGhostsWithoutGhosts=distToClosestCapsuleWithGhosts.pop() distToClosestCapsuleWithGhosts.sort(reverse=True) distToFurthestCapsuleWithGhosts=distToClosestCapsuleWithGhosts.pop() score=currentGameState.getScore() return score-(distToClosestCapsuleWithGhostsWithoutGhosts/10.)+(distToFurthestCapsuleWithGhosts/10.) class ContestHFOAttackingAgent(SearchAgent): def registerInitialState(self,state): self.start = state.whereAmI() self.foodLeftCount = len(state.food()) self.capsulesLeftCount = len(state.getCapsules()) self.startingHealthPoints=self.me().getHealthPoints()