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import numpy as np # Load the data from the file data = np.loadtxt("australian.txt") # Decision class symbols (assuming the last column represents the decision class) decision_classes = np.unique(data[:, -1]) print("Available Decision Classes:", decision_classes) # Size of decision classes class_sizes = {cls: np.sum(data[:, -1] == cls) for cls in decision_classes} print("Size of Decision Classes:") for cls, size in class_sizes.items(): print(f"Class {cls}: {size} objects") # Minimum and maximum values for numerical attributes min_values = np.min(data[:, :-1], axis=0) # Assuming the last column is the decision class max_values = np.max(data[:, :-1], axis=0) print("Minimum Values for Numerical Attributes:", min_values) print("Maximum Values for Numerical Attributes:", max_values) # Number of different available values for each attribute num_unique_values = [np.unique(data[:, i]).size for i in range(data.shape[1] - 1)] print("Number of Different Available Values for Each Attribute:", num_unique_values) # List of different available values for each attribute unique_values = [np.unique(data[:, i]) for i in range(data.shape[1] - 1)] print("List of Different Available Values for Each Attribute:") for i, values in enumerate(unique_values): print(f"Attribute {i+1}: {values}") # Standard deviation for numerical attributes in the whole system std_dev_whole_system = np.std(data[:, :-1], axis=0) print("Standard Deviation for Numerical Attributes in the Whole System:", std_dev_whole_system) # Standard deviation for numerical attributes in each decision class std_dev_per_class = {cls: np.std(data[data[:, -1] == cls, :-1], axis=0) for cls in decision_classes} print("Standard Deviation for Numerical Attributes in Each Decision Class:") for cls, std_dev in std_dev_per_class.items(): print(f"Class {cls}: {std_dev}")
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