Fixed some ambigous typos

Author: yarikoptic

causallearn/search/ConstraintBased/FCI.py

@@ -387,7 +387,7 @@ def doDdpOrientation(node_d: Node, node_a: Node, node_b: Node, node_c: Node, pre
                      independence_test_method, alpha: float, sep_sets: Dict[Tuple[int, int], Set[int]],
                      change_flag: bool, bk, verbose: bool = False) -> (bool, bool):
     """
-    Orients the edges inside the definte discriminating path triangle. Takes
+    Orients the edges inside the definite discriminating path triangle. Takes
     the left endpoint, and a,b,c as arguments.
     """
     if graph.is_adjacent_to(node_d, node_c):

causallearn/utils/DAG2CPDAG.py

@@ -99,7 +99,7 @@ def dag2cpdag(G: Dag) -> GeneralGraph:
             id2 = np.intersect1d(np.where(sign_edges == 0)[0], id1)
             sign_edges[id2] = -1  # label all "unknown" edges incident into y with "reversible"
 
-    # create CPDAG accoring the labelled edge
+    # create CPDAG according the labelled edge
     nodes = G.get_nodes()
     CPDAG = GeneralGraph(nodes)
     for m in range(M):

causallearn/utils/PCUtils/Helper.py

@@ -579,7 +579,7 @@ def contain_crrn_m(var, prt_m):
 def contain_common_neighbors_prt_mvar(X, Y, condition_set, skel, prt_m):
     """Check whether X and Y have at least one common neighbor.
     If they have no common neighbors,
-        return Flase
+        return False
     If they have common neighbors,
         but the common neighbors are not the parents of the missingness indicators of the variables in the test,
         return False

causallearn/utils/cit.py

@@ -338,8 +338,8 @@ def __call__(self, X, Y, condition_set=None):
         # Chi-square (or G-square) independence test.
         Xs, Ys, condition_set, cache_key = self.get_formatted_XYZ_and_cachekey(X, Y, condition_set)
         if cache_key in self.pvalue_cache: return self.pvalue_cache[cache_key]
-        indexs = condition_set + Xs + Ys
-        p = self.chisq_or_gsq_test(self.data[:, indexs].T, self.cardinalities[indexs], G_sq=self.method == 'gsq')
+        indexes = condition_set + Xs + Ys
+        p = self.chisq_or_gsq_test(self.data[:, indexes].T, self.cardinalities[indexes], G_sq=self.method == 'gsq')
         self.pvalue_cache[cache_key] = p
         return p