prism_algorithm.py added

Author: Anujmishra2005

Python/algorithms/graph_algorithms/prism_algorithm.py

@@ -0,0 +1,62 @@
+def prism(data, target):
+    rules = []
+    classes = set(row[target] for row in data)
+
+    for cls in classes:
+        cls_data = [row for row in data if row[target] == cls]
+        uncovered = cls_data.copy()
+
+        while uncovered:
+            rule = {}
+            remaining_features = list(data[0].keys())
+            remaining_features.remove(target)
+
+            while True:
+                best_feature, best_value, best_coverage = None, None, 0
+
+                for feature in remaining_features:
+                    values = set(row[feature] for row in uncovered)
+                    for value in values:
+                        coverage = [row for row in uncovered if row[feature] == value]
+                        if len(coverage) > best_coverage:
+                            best_coverage = len(coverage)
+                            best_feature = feature
+                            best_value = value
+
+                if best_feature is None:
+                    break
+
+                rule[best_feature] = best_value
+                uncovered = [row for row in uncovered if row[best_feature] == best_value]
+
+                if all(row[target] == cls for row in uncovered):
+                    rules.append((rule.copy(), cls))
+                    uncovered = [row for row in cls_data if not rule_matches(rule, row)]
+                    break
+
+                remaining_features.remove(best_feature)
+
+    return rules
+
+def rule_matches(rule, row):
+    for feature, value in rule.items():
+        if row[feature] != value:
+            return False
+    return True
+
+if __name__ == "__main__":
+    n = int(input("Enter number of rows: "))
+    features = input("Enter feature names separated by space (last one is target): ").split()
+    data = []
+
+    for _ in range(n):
+        values = input(f"Enter values for {features} separated by space: ").split()
+        row = {features[i]: values[i] for i in range(len(features))}
+        data.append(row)
+
+    target = features[-1]
+    rules = prism(data, target)
+
+    print("\nGenerated Rules:")
+    for r, cls in rules:
+        print(f"If {r} then {target} = {cls}")