Dev

Cargo.toml

@@ -10,4 +10,5 @@ crate-type = ["cdylib"]
 
 [dependencies]
 pyo3 = { version = "0.25.0", features = ["extension-module", "anyhow", "auto-initialize"] }
-numpy = "0.25"
+numpy = "0.25"
+sprs = "0.11"

python/fastlaps.py

@@ -7,33 +7,46 @@
 
 if __name__ == "__main__":
     # Quick example for a 3x3 matrix
-    matrix = np.random.rand(4, 5)
-    for algo in ["lapjv", "hungarian"]:
-        print(f"\nAlgorithm: {algo}")
-        start = time.time()
-        fastlap_cost, fastlap_rows, fastlap_cols = fastlap.solve_lap(matrix, algo)
-        fastlap_end = time.time()
-        print(f"fastlap.{algo}: Time={fastlap_end - start:.8f}s")
-        print(
-            f"fastlap.{algo}: Cost={fastlap_cost}, Rows={fastlap_rows}, Cols={fastlap_cols}"
-        )
-        if algo == "hungarian":
+    cols = 5
+    rows = 5
+    algos = ["lapjv", "hungarian", "lapmod"]
+    matrix = np.random.rand(rows, cols)
+    for i in range(10):
+
+        for algo in algos:
+            if algo != "lapjv":
+                continue
+            print(f"\nAlgorithm: {algo}")
             start = time.time()
-            scipy_rows, scipy_cols = linear_sum_assignment(matrix)
-            scipy_cost = matrix[scipy_rows, scipy_cols].sum()
-            scipy_end = time.time()
-            print(
-                f"scipy.optimize.linear_sum_assignment: Time={scipy_end - start:.8f}s"
-            )
-            print(
-                f"scipy.optimize.linear_sum_assignment: Cost={scipy_cost}, Rows={list(scipy_rows)}, Cols={list(scipy_cols)}"
-            )
-        if algo == "lapjv":
-            start = time.time()
-            lap_cost, lap_rows, lap_cols = lap.lapjv(matrix, extend_cost=True)
-            lap_end = time.time()
-            print(f"lap.lapjv: Time={lap_end - start:.8f}s")
-            print(f"lap.lapjv: Cost={lap_cost}, Rows={lap_rows}, Cols={lap_cols}")
+            fastlap_cost, fastlap_rows, fastlap_cols = fastlap.solve_lap(matrix, algo)
+            fastlap_end = time.time()
+            print(f"fastlap.{algo}: Time={fastlap_end - start:.8f}s")
+            # print(
+            #     f"fastlap.{algo}: Cost={fastlap_cost}, Rows={list(fastlap_rows)}, Cols={list(fastlap_cols)}"
+            # )
+            if algo == "hungarian":
+                start = time.time()
+                scipy_rows, scipy_cols = linear_sum_assignment(matrix)
+                scipy_cost = matrix[scipy_rows, scipy_cols].sum()
+                scipy_end = time.time()
+                print(
+                    f"scipy.optimize.linear_sum_assignment: Time={scipy_end - start:.8f}s"
+                )
+                print(
+                    f"scipy.optimize.linear_sum_assignment: Cost={scipy_cost}, Rows={list(scipy_rows)}, Cols={list(scipy_cols)}"
+                )
+            if algo == "lapjv":
+                start = time.time()
+                lap_cost, lap_rows, lap_cols = lap.lapjv(matrix, extend_cost=True)
+                lap_end = time.time()
+                print(f"lap.lapjv: Time={lap_end - start:.8f}s")
+                # print(f"lap.lapjv: Cost={lap_cost}, Rows={lap_rows}, Cols={lap_cols}")
+            if algo == "lapmod":
+                start = time.time()
+                lapmod_cost, lapmod_rows, lapmod_cols = lap.lapmod(matrix)
+                lapmod_end = time.time()
+                print(f"lap.lapmod: Time={lapmod_end - start:.8f}s")
+                # print(f"lap.lapmod: Cost={lapmod_cost}, Rows={lapmod_rows}, Cols={lapmod_cols}")
 
 """
 First release:
@@ -49,4 +62,5 @@
 fastlap.hungarian: Cost=0.7465856501551806, Rows=[2, 0, 1, 3], Cols=[1, 2, 0, 3, 18446744073709551615]
 scipy.optimize.linear_sum_assignment: Time=0.00001287s
 scipy.optimize.linear_sum_assignment: Cost=0.6229432588732741, Rows=[0, 1, 2, 3], Cols=[2, 0, 1, 4]
+
 """