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| from cosmic_pi_network.cosmic_network import CosmicPiNetwork | ||
| from cosmic_pi_network.data_processing import clean_data, preprocess_data | ||
| from cosmic_pi_network.data_analysis import analyze_data | ||
| from cosmic_pi_network.models import train_model, evaluate_model | ||
| from cosmic_pi_network.visualization import plot_results | ||
| from cosmic_pi_network.config import COSMIC_PI_IP, COSMIC_PI_PORT | ||
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| # Initialize the network object | ||
| network = CosmicPiNetwork(ip=COSMIC_PI_IP, port=COSMIC_PI_PORT) | ||
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| # Connect to the network | ||
| network.connect() | ||
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| # Receive raw data from the network | ||
| raw_data = network.receive_data() | ||
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| # Clean and preprocess the data | ||
| cleaned_data = clean_data(raw_data) | ||
| preprocessed_data = preprocess_data(cleaned_data) | ||
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| # Analyze the data | ||
| analysis_results = analyze_data(preprocessed_data) | ||
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| # Train and evaluate the model | ||
| model = train_model(analysis_results) | ||
| model_accuracy = evaluate_model(model, analysis_results) | ||
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| # Visualize the results | ||
| plot_results(analysis_results) | ||
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| # Disconnect from the network | ||
| network.disconnect() | ||
| from cosmic_pi_network.api import API | ||
| from cosmic_pi_network.computer_vision import ComputerVision | ||
| from cosmic_pi_network.data_preprocessing import DataPreprocessing | ||
| from cosmic_pi_network.neural_network import NeuralNetwork | ||
| from cosmic_pi_network.nlp import NLP | ||
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| def main(): | ||
| api = API() | ||
| weather_data = api.get_weather("New York") | ||
| print("Weather data:", weather_data) | ||
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| nlp = NLP() | ||
| text = "This is a sample text." | ||
| tokens = nlp.tokenize_text(text) | ||
| filtered_tokens = nlp.remove_stopwords(tokens) | ||
| lemmatized_tokens = nlp.lemmatize_tokens(filtered_tokens) | ||
| sentiment = nlp.sentiment_analysis(text) | ||
| print("Sentiment:", sentiment) | ||
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| computer_vision = ComputerVision() | ||
| image = computer_vision.load_image("image.jpg") | ||
| grayscale_image = computer_vision.convert_to_grayscale(image) | ||
| thresh_image = computer_vision.apply_threshold(grayscale_image) | ||
| edges_image = computer_vision.detect_edges(grayscale_image) | ||
| faces = computer_vision.detect_faces(grayscale_image) | ||
| print("Number offaces detected:", len(faces)) | ||
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| data_preprocessing = DataPreprocessing() | ||
| data = data_preprocessing.load_data("data.csv") | ||
| X_train, X_test, y_train, y_test = data_preprocessing.split_data(data) | ||
| X_train_scaled, X_test_scaled = data_preprocessing.scale_data(X_train, X_test) | ||
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| neural_network = NeuralNetwork() | ||
| model = neural_network.create_model((28, 28, 1), 10) | ||
| neural_network.train_model(model, X_train_scaled, y_train) | ||
| loss, accuracy = neural_network.evaluate_model(model, X_test_scaled, y_test) | ||
| print("Loss:", loss, "Accuracy:", accuracy) | ||
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| if __name__ == "__main__": | ||
| main() |