mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 14:54:07 +00:00
117 lines
5.2 KiB
Python
117 lines
5.2 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error
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from spec_read import all_spectral_data
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def prepare_data(data):
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"""Calculate the average spectral values and their gradients for each fruit across all pixels."""
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avg_spectra = np.mean(data, axis=(1, 2))
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gradients = np.gradient(avg_spectra, axis=1)
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second_gradients = np.gradient(gradients, axis=1)
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return avg_spectra, gradients, second_gradients
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def train_model(X, y):
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"""Train a RandomForest model."""
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rf = RandomForestRegressor(n_estimators=100)
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rf.fit(X, y)
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return rf
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def split_data(X, y, test_size=0.20, random_state=2):
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"""Split data into training and test sets."""
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return train_test_split(X, y, test_size=test_size, random_state=random_state)
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def evaluate_model(model, X_test, y_test):
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"""Evaluate the model and return MSE and predictions."""
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y_pred = model.predict(X_test)
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mse = mean_squared_error(y_test, y_pred)
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return mse, y_pred
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def print_predictions(y_test, y_pred):
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"""Print actual and predicted values."""
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print("Test Set Predictions:")
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for i, (real, pred) in enumerate(zip(y_test, y_pred)):
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print(f"Sample {i + 1}: True Value = {real:.2f}, Predicted Value = {pred:.2f}")
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def plot_spectra(X, y):
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"""Plot the average spectra for all samples and annotate with sweetness_acidity values."""
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plt.figure(figsize=(10, 6))
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for i in range(X.shape[0]):
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plt.plot(X[i], label=f'Sample {i+1}')
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plt.annotate(f'{y[i]:.1f}', xy=(len(X[i])-1, X[i][-1]), xytext=(5, 0),
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textcoords='offset points', ha='left', va='center')
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plt.xlabel('Wavelength Index')
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plt.ylabel('Average Spectral Value')
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plt.title('Average Spectral Curves for All Samples')
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plt.legend(loc='upper right', bbox_to_anchor=(1.1, 1.05))
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plt.show()
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def plot_gradients(gradients):
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"""Plot the gradient of the average spectra for all samples."""
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plt.figure(figsize=(10, 6))
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for i in range(gradients.shape[0]):
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plt.plot(gradients[i], label=f'Sample {i+1}')
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plt.xlabel('Wavelength Index')
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plt.ylabel('Gradient Value')
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plt.title('Gradient of Average Spectral Curves for All Samples')
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plt.legend(loc='upper right', bbox_to_anchor=(1.1, 1.05))
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plt.show()
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def plot_second_gradients(second_gradients):
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"""Plot the second gradient of the average spectra for all samples."""
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plt.figure(figsize=(10, 6))
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for i in range(second_gradients.shape[0]):
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plt.plot(second_gradients[i], label=f'Sample {i+1}')
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plt.xlabel('Wavelength Index')
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plt.ylabel('Second Gradient Value')
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plt.title('Second Gradient of Average Spectral Curves for All Samples')
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plt.legend(loc='upper right', bbox_to_anchor=(1.1, 1.05))
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plt.show()
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def main():
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sweetness_acidity = np.array([
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16.2, 16.1, 17, 16.9, 16.8, 17.8, 18.1, 17.2, 17, 17.2, 17.1, 17.2,
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17.2, 17.2, 18.1, 17, 17.6, 17.4, 17.1, 17.1, 16.9, 17.6, 17.3, 16.3,
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16.5, 18.7, 17.6, 16.2, 16.8, 17.2, 16.8, 17.3, 16, 16.6, 16.7, 16.7,
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17.3, 16.3, 16.8, 17.4, 17.3, 16.3, 16.1, 17.2, 18.6, 16.8, 16.1, 17.2,
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18.3, 16.5, 16.6, 17, 17, 17.8, 16.4, 18, 17.7, 17, 18.3, 16.8, 17.5,
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17.7, 18.5, 18, 17.7, 17, 18.3, 18.1, 17.4, 17.7, 17.8, 16.3, 17.1, 16.8,
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17.2, 17.5, 16.6, 17.7, 17.1, 17.7, 19.4, 20.3, 17.3, 15.8, 18, 17.7,
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17.2, 15.2, 18, 18.4, 18.3, 15.7, 17.2, 18.6, 15.6, 17, 16.9, 17.4, 17.8,
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16.5
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])
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X_avg, X_grad, X_second_grad = prepare_data(all_spectral_data)
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plot_spectra(X_avg, sweetness_acidity) # Plot average spectral curves
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plot_gradients(X_grad) # Plot gradient curves
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plot_second_gradients(X_second_grad) # Plot second gradient curves
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# Train and evaluate using average spectral values
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X_train_avg, X_test_avg, y_train_avg, y_test_avg = split_data(X_avg, sweetness_acidity)
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rf_model_avg = train_model(X_train_avg, y_train_avg)
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mse_avg, y_pred_avg = evaluate_model(rf_model_avg, X_test_avg, y_test_avg)
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print("Mean Squared Error using average spectral values:", mse_avg)
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# Train and evaluate using first gradients
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X_train_grad, X_test_grad, y_train_grad, y_test_grad = split_data(X_grad, sweetness_acidity)
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rf_model_grad = train_model(X_train_grad, y_train_grad)
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mse_grad, y_pred_grad = evaluate_model(rf_model_grad, X_test_grad, y_test_grad)
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print("Mean Squared Error using first gradients:", mse_grad)
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# Train and evaluate using second gradients
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X_train_second_grad, X_test_second_grad, y_train_second_grad, y_test_second_grad = split_data(X_second_grad, sweetness_acidity)
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rf_model_second_grad = train_model(X_train_second_grad, y_train_second_grad)
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mse_second_grad, y_pred_second_grad = evaluate_model(rf_model_second_grad, X_test_second_grad, y_test_second_grad)
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print("Mean Squared Error using second gradients:", mse_second_grad)
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print("Predictions using average spectral values:")
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print_predictions(y_test_avg, y_pred_avg)
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print("Predictions using first gradients:")
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print_predictions(y_test_grad, y_pred_grad)
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print("Predictions using second gradients:")
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print_predictions(y_test_second_grad, y_pred_second_grad)
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if __name__ == "__main__":
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main() |