fix determinization

elazarg · Aug 4, 2024 · c3970d0 · c3970d0
1 parent b95f1d1
commit c3970d0
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Showing 4 changed files with 8 additions and 4 deletions.
diff --git a/experiment/k_means/instrumented.py b/experiment/k_means/instrumented.py
@@ -2,6 +2,8 @@
 import numpy as np
 import sklearn.datasets
 
+np.random.seed(42)
+
 
 def run(X: np.ndarray, k: int, max_iterations: int) -> np.ndarray:
     """A simple clustering method that forms k clusters by iteratively reassigning
@@ -15,7 +17,6 @@ def run(X: np.ndarray, k: int, max_iterations: int) -> np.ndarray:
         The number of iterations the algorithm will run for if it does
         not converge before that.
     """
-    np.random.seed(42)
     nsamples, features = X.shape
     centroids = X[np.random.choice(nsamples, k)]
     clusters = list[list[int]]()

diff --git a/experiment/k_means/main.py b/experiment/k_means/main.py
@@ -1,6 +1,8 @@
 import numpy as np
 import sklearn.datasets
 
+np.random.seed(42)
+
 
 def run(X: np.ndarray, k: int, max_iterations: int) -> np.ndarray:
     """A simple clustering method that forms k clusters by iteratively reassigning
@@ -14,7 +16,6 @@ def run(X: np.ndarray, k: int, max_iterations: int) -> np.ndarray:
         The number of iterations the algorithm will run for if it does
         not converge before that.
     """
-    np.random.seed(42)
     nsamples, features = X.shape
     centroids = X[np.random.choice(nsamples, k)]
     clusters = list[list[int]]()

diff --git a/experiment/k_means/naive.py b/experiment/k_means/naive.py
@@ -2,6 +2,8 @@
 import numpy as np
 import sklearn.datasets
 
+np.random.seed(42)
+
 
 def run(X: np.ndarray, k: int, max_iterations: int) -> np.ndarray:
     """A simple clustering method that forms k clusters by iteratively reassigning
@@ -30,7 +32,6 @@ def run(X: np.ndarray, k: int, max_iterations: int) -> np.ndarray:
         sample_i,
         y_pred,
     ] = (None,) * 13
-    np.random.seed(42)
     nsamples, features = X.shape
     centroids = X[np.random.choice(nsamples, k)]
     clusters = list[list[int]]()

diff --git a/experiment/k_means/vm.py b/experiment/k_means/vm.py
@@ -2,6 +2,8 @@
 import numpy as np
 import sklearn.datasets
 
+np.random.seed(42)
+
 
 def run(X: np.ndarray, k: int, max_iterations: int) -> np.ndarray:
     """A simple clustering method that forms k clusters by iteratively reassigning
@@ -15,7 +17,6 @@ def run(X: np.ndarray, k: int, max_iterations: int) -> np.ndarray:
         The number of iterations the algorithm will run for if it does
         not converge before that.
     """
-    np.random.seed(42)
     nsamples, features = X.shape
     centroids = X[np.random.choice(nsamples, k)]
     clusters = list[list[int]]()