reglage conflit

hello_world.py

+
 print("hello world")
 
 
@@ -29,3 +30,56 @@ print(aml.leaderboard)
 print(aml.leader)
 
 print(aml.predict(test))
+
+import numpy as np
+def generate_checkers(nb_app, nb_test, cov=[[0.25,0],[0,0.25]]):
+    print(cov)
+    xapp=np.ndarray((nb_app*16,2))
+    yapp=np.ndarray(nb_app*16)
+    xtest=np.ndarray((nb_test*16,2))
+    ytest=np.ndarray(nb_test*16)
+
+    y=1
+    index_app=0
+    index_test=0
+    for i in range(0,4):
+        for j in range(0,4):
+            center = [i,j]
+            y = 1 if (i+j) % 2 else -1
+            xapp[index_app:index_app+nb_app] = np.random.multivariate_normal(center,cov,nb_app)
+            yapp[index_app:index_app+nb_app] = np.ones(nb_app)*y
+            
+            xtest[index_test:index_test+nb_test] = np.random.multivariate_normal(center,cov,nb_test)
+            ytest[index_test:index_test+nb_test] = np.ones(nb_test)*y
+
+            index_test += nb_test
+            index_app += nb_app
+          
+    return xapp, yapp, xtest, ytest
+
+def generate_linear_separable_case(nb_app,nb_test):
+    from sklearn.datasets import make_blobs
+    x, y = make_blobs(n_samples=nb_app+nb_test, centers=2, n_features=2)
+    x_app = x[0:nb_app,:]
+    y_app = y[0:nb_app].astype(np.int64)
+    
+    x_test = x[nb_app:,:]
+    y_test = y[nb_app:]
+    
+    y_app[y_app == 0] = -1
+    y_test[y_test == 0 ] = -1
+    return x_app, y_app, x_test, y_test
+
+def generate_xor(nb_app,nb_test):
+    np.random.seed(0)
+    x_app = np.random.randn(nb_app, 2)
+    y_app = np.logical_xor(x_app[:, 0] > 0, x_app[:, 1] > 0).astype(int)
+    y_app[y_app == 0] = -1
+    
+    np.random.seed(0)
+    x_test = np.random.randn(nb_test, 2)
+    y_test = np.logical_xor(x_test[:, 0] > 0, x_test[:, 1] > 0).astype(int)
+    y_test[y_test == 0] = -1
+    
+    return x_app,y_app,x_test,y_test
+