fix tests

phuzzy/approx/doe.py

@@ -66,7 +66,7 @@ def predict(self, name=None):
         self.results_prediction = pd.DataFrame({"res": y, "alpha": self.doe_prediction.samples.alpha})
         print(1, self.results_training.head())
         print(2, self.results_prediction.head())
-        df_res = pd.concat([self.results_training, self.results_prediction[["res", "alpha"]]])
+        df_res = pd.concat([self.results_training, self.results_prediction[["res", "alpha"]]], sort=False)
 
         if name is None:
             name = "z"

phuzzy/data/plots.py

@@ -54,13 +54,8 @@ def bootstrapping(data, df_boot, show=False):
 
     return fig, axs
 
-<<<<<<< HEAD
 def plot_hist(x, ax=None, bins=None, normed=False, color=None, **kwargs):
     x = x[~np.isnan(x)]
-=======
-def plot_hist(x, ax=None, bins=None, normed=True, **kwargs):
-
->>>>>>> aa69c3564895524ef928f88a5162c36cd9cf871f
     if bins is None:
         bins = 'auto'
     bins, edges = np.histogram(x, bins=bins)

phuzzy/shapes/__init__.py

@@ -843,14 +843,26 @@ def defuzzification_p50(self):
         """defuzzification mean my means of ppf(0.5)"""
         return self.ppf(.5)
 
-    def defuzzification_centroid(self):
+    def defuzzification_centroid2(self):
         """defuzzification center of gravity"""
         # x = self.df[["l", "r"]].values.flatten()
         x = np.linspace(self.min(), self.max(), 100001)
         m = self.alpha(x)
         cg = np.sum(x * m) / np.sum(m)
         return cg
 
+    def defuzzification_centroid(self):
+        """defuzzification center of gravity"""
+        df = pd.concat([self.df[["l", "alpha"]].rename(columns={"l":"x"}), self.df[["alpha", "r"]].rename(columns={"r":"x"})]).sort_values(by=["x", "alpha"])
+        df = df.drop_duplicates(subset="x", keep="last")
+        d = pd.DataFrame()
+        d["alpha"] = df.alpha.rolling(2).mean()
+        d["dx"] = (df.x.rolling(2).max() - df.x.rolling(2).min())
+        d["x"] = df.x.rolling(2).mean()
+        d["A"] = d.dx * d.alpha
+        d["Ax"] = d.x * d.A
+        return d.Ax.sum()/d.A.sum()
+
     def defuzzification(self, method='centroid'):
 
         # self.zmax_values = np.flip(self.zmax_values, 0)