initial commit

2026-03-02 20:55:36 +09:00
commit f224644b49
86 changed files with 1065250 additions and 0 deletions
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+import numpy as np
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import r2_score
+
+def fit_op_cost_functional(df, algo):
+    sub = df[(df["algorithm"] == algo) & (df["reached"] == True)].copy()
+
+    sub["logN"] = np.log(sub["nodes"])
+    sub = sub.rename(columns={"relax_success":"decrease_key_calls",
+                              "nodes":"add_calls"})
+
+    if algo == "binary":
+        sub["f_extract"] = sub["extract_min_calls"] * sub["logN"]
+        sub["f_decrease"] = sub["decrease_key_calls"] * sub["logN"]
+    else:  # fibonacci
+        sub["f_extract"] = sub["extract_min_calls"] * sub["logN"]
+        sub["f_decrease"] = sub["decrease_key_calls"]
+
+    X = sub[["add_calls", "f_extract", "relax_attempts", "f_decrease"]].to_numpy()
+    y = sub["time"].to_numpy()
+
+    model = LinearRegression()
+    model.fit(X, y)
+
+    y_pred = model.predict(X)
+    r2 = r2_score(y, y_pred)
+
+    return {
+        "algo": algo,
+        "intercept": model.intercept_,
+        "coef_add": model.coef_[0],
+        "coef_extract": model.coef_[1],
+        "coef_relax": model.coef_[2],
+        "coef_decrease": model.coef_[3],
+        "r2": r2,
+        "n_samples": len(sub)
+    }
+
+def op_cost_functional_analysis(df):
+    return pd.DataFrame([
+        fit_op_cost_functional(df, "binary"),
+        fit_op_cost_functional(df, "fibonacci")
+    ])
+
+df = pd.read_csv("results/synthetic_data/raw/20260302_183202.csv")
+summary = op_cost_functional_analysis(df)
+print(summary)