primate 0.2.0

Primate AI: Tools for decision-making and NLP.

Primate AI is a Python package that bridges decision-making and natural language processing, inspired by intelligent behavior.

Features

• Decision-Making Models: - Game-theoretic strategies (e.g., tit-for-tat). - Multi-agent simulations. - Reinforcement learning agents.

• Natural Language Processing: - Sentiment analysis. - Tokenization, text similarity, and vectorization. - Language evolution simulation.

• Utility Tools: - Logging and configuration management. - Performance profiling for PyTorch models. - Device helpers for GPU-accelerated computations.

Installation

You can install the package via pip:

pip install primate

Quick Start

Here are a few examples to get started:

### 1. Decision Agent

Simulate decision-making with game-theoretic strategies:

from primate.decision.agent import DecisionAgent

# Create an agent with a strategy
agent = DecisionAgent(strategy="tit_for_tat")
decision = agent.decide(opponent_action="cooperate")
print(f"Agent decided to: {decision}")

### 2. Sentiment Analysis

Analyze the sentiment of text data:

from primate.nlp.sentiment import SentimentAnalyzer

     # Initialize sentiment analyzer
     analyzer = SentimentAnalyzer()
     sentiment = analyzer.analyze("I love bananas!")
     print(f"Sentiment: {sentiment}")  # Output: "positive"

### 3. Tokenization with Transformers

Tokenize text using pre-trained transformer models:

from primate.nlp.tokenizer import Tokenizer

# Initialize tokenizer
tokenizer = Tokenizer(model_name="bert-base-uncased")
tokenized = tokenizer.tokenize(["I love Python!", "NLP is fun."])
print("Input IDs:", tokenized["input_ids"])
print("Attention Masks:", tokenized["attention_mask"])

### 4. Model Profiling

Profile PyTorch models for parameter and operation details:

from primate.utils.helpers import PerformanceHelper
import torch.nn as nn

# Define a simple model
class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.linear = nn.Linear(10, 5)

    def forward(self, x):
        return self.linear(x)

model = SimpleModel()
profile = PerformanceHelper.profile_model(model, input_size=(1, 10))
print(profile)

License

This project is licensed under the MIT License.