|
| 1 | +def prism(data, target): |
| 2 | + rules = [] |
| 3 | + classes = set(row[target] for row in data) |
| 4 | + |
| 5 | + for cls in classes: |
| 6 | + cls_data = [row for row in data if row[target] == cls] |
| 7 | + uncovered = cls_data.copy() |
| 8 | + |
| 9 | + while uncovered: |
| 10 | + rule = {} |
| 11 | + remaining_features = list(data[0].keys()) |
| 12 | + remaining_features.remove(target) |
| 13 | + |
| 14 | + while True: |
| 15 | + best_feature, best_value, best_coverage = None, None, 0 |
| 16 | + |
| 17 | + for feature in remaining_features: |
| 18 | + values = set(row[feature] for row in uncovered) |
| 19 | + for value in values: |
| 20 | + coverage = [row for row in uncovered if row[feature] == value] |
| 21 | + if len(coverage) > best_coverage: |
| 22 | + best_coverage = len(coverage) |
| 23 | + best_feature = feature |
| 24 | + best_value = value |
| 25 | + |
| 26 | + if best_feature is None: |
| 27 | + break |
| 28 | + |
| 29 | + rule[best_feature] = best_value |
| 30 | + uncovered = [row for row in uncovered if row[best_feature] == best_value] |
| 31 | + |
| 32 | + if all(row[target] == cls for row in uncovered): |
| 33 | + rules.append((rule.copy(), cls)) |
| 34 | + uncovered = [row for row in cls_data if not rule_matches(rule, row)] |
| 35 | + break |
| 36 | + |
| 37 | + remaining_features.remove(best_feature) |
| 38 | + |
| 39 | + return rules |
| 40 | + |
| 41 | +def rule_matches(rule, row): |
| 42 | + for feature, value in rule.items(): |
| 43 | + if row[feature] != value: |
| 44 | + return False |
| 45 | + return True |
| 46 | + |
| 47 | +if __name__ == "__main__": |
| 48 | + n = int(input("Enter number of rows: ")) |
| 49 | + features = input("Enter feature names separated by space (last one is target): ").split() |
| 50 | + data = [] |
| 51 | + |
| 52 | + for _ in range(n): |
| 53 | + values = input(f"Enter values for {features} separated by space: ").split() |
| 54 | + row = {features[i]: values[i] for i in range(len(features))} |
| 55 | + data.append(row) |
| 56 | + |
| 57 | + target = features[-1] |
| 58 | + rules = prism(data, target) |
| 59 | + |
| 60 | + print("\nGenerated Rules:") |
| 61 | + for r, cls in rules: |
| 62 | + print(f"If {r} then {target} = {cls}") |
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