git leftover
@@ -449,15 +449,11 @@ estimate the variances. See paper "Variance estimation when using inverse probab
-------
beta: (1,d) numpy array.
"""
-<<<<<<< HEAD
- self.path: List[ndarray] = []
- assert precision <= 1.0, "precision must be less than or equal to 1."
-=======
+
decision = BatchVsSingle.decide(self._batch_mode, T.nunique(), X.shape[0], X.shape[1])
get_gradients = getattr(self, "_get_efron_values_%s" % decision)
self._batch_mode = decision == "batch"
->>>>>>> refactor newtons method in CoxPH and delay checking correlations
_, d = X.shape
self.path = []
