FBI树python

### FBI Tree Implementation in Python An FBI tree is not a standard data structure or algorithm within common computer science literature, which suggests that this might be either a specialized term used in specific contexts or possibly confused with another concept. However, based on typical conventions for creating trees and binary structures in Python, an interpretation can be made. For demonstration purposes, let's assume "FBI" stands for a custom type of Binary Feature Inspection tree designed to inspect features at each node as part of some decision-making process similar to how nodes operate in other types of decision trees but specifically tailored towards certain applications like those mentioned in Windmill’s capabilities such as handling different programming languages including Python[^1]. Below is an example implementation: ```python class Node: def __init__(self, feature=None, threshold=None, left=None, right=None, value=None): self.feature = feature # The attribute being inspected. self.threshold = threshold # Threshold for splitting (if applicable). self.left = left # Left child node. self.right = right # Right child node. self.value = value # Value if it's a leaf node. def build_fbi_tree(data, labels): """Builds an FBI tree recursively.""" # Base case: If all examples have same label -> create leaf node. unique_labels = set(labels) if len(unique_labels) == 1: return Node(value=unique_labels.pop()) # Another base case could involve stopping criteria related to depth, # number of samples, etc., depending on requirements. # For simplicity, choose first feature as split criterion here; # In practice, more sophisticated methods would select optimal splits. best_feature_index = 0 # Split dataset into two parts according to chosen feature. true_data, false_data = [], [] true_labels, false_labels = [], [] for i, sample in enumerate(data): if sample[best_feature_index]: true_data.append(sample) true_labels.append(labels[i]) else: false_data.append(sample) false_labels.append(labels[i]) # Recursively construct subtrees. left_subtree = build_fbi_tree(true_data, true_labels) right_subtree = build_fbi_tree(false_data, false_labels) return Node(feature=best_feature_index, left=left_subtree, right=right_subtree) def predict(node, instance): """Predict using the constructed FBI tree.""" if node.value is not None: return node.value if instance[node.feature]: return predict(node.left, instance) else: return predict(node.right, instance) ``` This code defines a simple version of what might constitute an FBI tree where decisions are made based on whether elements meet conditions defined by thresholds associated with particular features. Note that actual implementations may vary widely based on intended functionality beyond basic inspection tasks described above.