import numpy as np
from sklearn import preprocessing
from scipy.optimize import minimize
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from keras.models import load_model

class Phi_Fractal_Network:
    def __init__(self):
        self.network = None
    
    def build_network(self):
        # Construct the network using phi-fractal principles
        pass
    
    def process_input(self, data):
        # Preprocess and normalize input data
        return normalized_data
    
    def mathematical_modeling(self, data):
        # Perform linear algebra operations, probability calculations, etc.
        result = self.network.dot(data)
        return result

class NaturalLanguageProcessing:
    def __init__(self):
        self.tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
        self.model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
    
    def tokenize_input(self, text):
        tokens = word_tokenize(text)
        return [self.tokenizer(word) for word in tokens]
    
    def analyze_text(self, text):
        inputs = self.tokenize_input(text)
        outputs = self.model(inputs)
        return outputs[0].tolist()

class SemanticAnalysis:
    def __init__(self):
        self.embedding_model = load_model('path_to_word2vec_model')
    
    def vectorize_text(self, text):
        tokens = word_tokenize(text)
        vectors = [self.embedding_model[word] for word in tokens]
        return np.mean(vectors, axis=0)

class LogicalReasoning:
    def __init__(self):
        self.lemmatizer = WordNetLemmatizer()
    
    def preprocess_sentence(self, sentence):
        lemmatized = ' '.join([self.lemmatizer.lemmatize(word) for word in word_tokenize(sentence)])
        return lemmatized

class ReinforcementLearningAgent:
    def __init__(self):
        self.agent = QLearningAgent()
    
    def learn_from_environment(self, state, action, reward):
        self.agent.learn(state, action, reward)
    
    def take_action(self, state):
        return self.agent.choose_best_action(state)

# Example usage
phi_network = Phi_Fractal_Network()
nlp_module = NaturalLanguageProcessing()
semantic_analysis = SemanticAnalysis()
logical_reasoning = LogicalReasoning()
rl_agent = ReinforcementLearningAgent()

text_input = "What is the solution to 2x + 3y = 5?"
preprocessed_text = nlp_module.preprocess_sentence(text_input)
semantic_vector = semantic_analysis.vectorize_text(preprocessed_text)

# Further processing and integration with other components