Dass333

import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.mixture import GaussianMixture # 1. Simulate radiometric input data (e.g., K, eU, eTh channels) np.random.seed(42) data_points = np.random.rand(1000, 3) * 100 # 2. Run K-Means Clustering to partition the array (similar to K-means22) kmeans = KMeans(n_clusters=22, random_state=0, n_init="auto") kmeans_labels = kmeans.fit_predict(data_points) # 3. Run a Gaussian Mixture Model for high-density probability matching gmm = GaussianMixture(n_components=10, random_state=0) gmm_labels = gmm.fit_predict(data_points) # 4. Generate the simplified visualization matrix plt.figure(figsize=(10, 5)) plt.scatter(data_points[:, 0], data_points[:, 1], c=kmeans_labels, cmap='tab20', s=10) plt.title("DASS333 Correlated Spatial Clustering Array") plt.xlabel("Spectral Channel Alpha") plt.ylabel("Spectral Channel Beta") plt.colorbar(label="Cluster Assignment") plt.show() Use code with caution. 📈 Future Outlook: Automated Spatial Pipelines

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Based on research concerning gamma-ray spectrometry and RGB mapping of geological terrains, is a notation associated with the classification of specific granitic rock units based on their radioelement signatures. It is commonly used within:

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) and assign them to color channels. The specific signature of in these images allows for the rapid identification of specific granite outcrops. 2. GMM and K-means Clustering

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