tests: use fixed initialization of x in sparse-dense test

tests/test_basic.py

@@ -33,26 +33,40 @@ def test_simple_problem():
 
 def test_sparse_dense_standardized():
     """Test case for checking standardization works for both sparse and dense."""
-    n = 10
-    p = 3
+    x_dense = np.array(
+        [
+            [0.0, 0.13339576, 0.49361983],
+            [0.17769259, 0.66565742, 0.36972579],
+            [0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.94280368],
+            [0.0, 0.0, 0.3499374],
+            [0.0, 0.22377115, 0.0],
+            [0.0, 0.96893287, 0.95858229],
+            [0.70486475, 0.60885162, 0.0],
+            [0.0, 0.92902639, 0.0],
+            [0.4978676, 0.0, 0.50022619],
+        ]
+    )
+
+    x_sparse = csc_array(x_dense)
 
-    rng = default_rng(4)
-    x = csc_array(random(n, p, density=0.5, random_state=rng))
     beta = np.array([1.0, 2, -0.9])
-    y = x @ beta
+    y = x_dense @ beta
 
     lam = np.array([0.5, 0.5, 0.2])
     alpha = 1.0
 
-    model = Slope(lam, alpha, standardize=True)
+    model = Slope(lam, alpha, standardize=True, fit_intercept=False)
 
     coef_true = np.array([[0.04258934], [0.74274634], [-0.02910647]])
 
-    model.fit(x, y)
+    model.fit(x_dense, y)
     coef_sparse = model.coef_
 
-    model.fit(x.toarray(), y)
-    coef_dense = model.coef_
+    model2 = Slope(lam, alpha, standardize=True, fit_intercept=False)
+
+    model2.fit(x_sparse, y)
+    coef_dense = model2.coef_
 
     np.testing.assert_array_almost_equal(coef_sparse, coef_dense)
     np.testing.assert_array_almost_equal(coef_dense, coef_true)