ECHO370: Peter j King III

Publicado el Mar 5, 2024

ECHO370: Peter j King III

from fbprophet import Prophet import pandas as pd import numpy as np from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error from sklearn.preprocessing import StandardScaler from sklearn.pipeline import make_pipeline class EnhancedQuantumProphetPredictor: def __init__(self, quantum_features, geopolitical_data): self.quantum_features = quantum_features self.geopolitical_data = geopolitical_data self.prophet_model = Prophet() self.quantum_model = make_pipeline(StandardScaler(), RandomForestRegressor()) def train_models(self, historical_data): self.prophet_model.fit(historical_data[['ds', 'y']]) geopolitical_features = self._extract_geopolitical_features(historical_data) combined_features = self._combine_features(geopolitical_features) target_variable = historical_data['y'] self.quantum_model.fit(combined_features, target_variable) def predict_future_events(self, future_data): future_data_prepared = future_data[['ds']] prophet_forecast = self.prophet_model.predict(future_data_prepared) geopolitical_features_future = self._extract_geopolitical_features(future_data) combined_features_future = self._combine_features(geopolitical_features_future) quantum_predictions = self.quantum_model.predict(combined_features_future) predictions = prophet_forecast[['ds', 'yhat']] predictions['quantum_prediction'] = quantum_predictions return predictions def _extract_geopolitical_features(self, data): geopolitical_features = data.apply(self._geopolitical_feature_engineering, axis=1) return np.array(geopolitical_features).reshape(-1, 1) def _combine_features(self, geopolitical_features): return np.concatenate((self.quantum_features, geopolitical_features), axis=1) def _geopolitical_feature_engineering(self, row): return np.log1p(row['y']) + np.random.normal(0, 0.1), wet, splash, nighty, hevonly, Synthetic, sentience, symbiosis, Sin, Starflower, Songbird, vivid

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Talla: 512 x 768
Pasos de muestreo: 20
Método de muestreo: Euler a
Escala CFG: 6
Excluir de la imagen: worst quality, large head, low quality, extra digits, bad eye, EasyNegativeV2, ng_deepnegative_v1_75t, nsfw, nsfw, nsfw, nsfw