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venv/lib/python3.9/site-packages/sklearn/decomposition/_cdnmf_fast.pyx

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+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from cython cimport floating
+from libc.math cimport fabs
+
+
+def _update_cdnmf_fast(floating[:, ::1] W, floating[:, :] HHt,
+                       floating[:, :] XHt, Py_ssize_t[::1] permutation):
+    cdef:
+        floating violation = 0
+        Py_ssize_t n_components = W.shape[1]
+        Py_ssize_t n_samples = W.shape[0]  # n_features for H update
+        floating grad, pg, hess
+        Py_ssize_t i, r, s, t
+
+    with nogil:
+        for s in range(n_components):
+            t = permutation[s]
+
+            for i in range(n_samples):
+                # gradient = GW[t, i] where GW = np.dot(W, HHt) - XHt
+                grad = -XHt[i, t]
+
+                for r in range(n_components):
+                    grad += HHt[t, r] * W[i, r]
+
+                # projected gradient
+                pg = min(0., grad) if W[i, t] == 0 else grad
+                violation += fabs(pg)
+
+                # Hessian
+                hess = HHt[t, t]
+
+                if hess != 0:
+                    W[i, t] = max(W[i, t] - grad / hess, 0.)
+
+    return violation