Update Linear.py

Linear.py

@@ -1,50 +1,51 @@
-#sk-learn風の線形回帰分析クラス
-#SE, t-val,p-val, R2を出力
-#x, yをpandas DataFrameで入力、self.olsは(coef, SE, t値, p値)のDataFrame
-from scipy.stats import t
-
-class linear_regression():
-    def __init__(self, fit_intercept = True):
-      self.const = fit_intercept
-
-    def fit(self, x, y):
-      if self.const ==1:
-        z = np.concatenate([np.ones((x.shape[0],1)), x], axis=1)
-        self.index = x.columns.insert(0,'const')
-      else:
-        z = x
-        self.index = x.columns
-      self.phi = np.linalg.inv(z.T @ z) @ (z.T @ y)
-      if self.const == 1:
-        self.intercept_ = self.phi[0]
-        self.coef_ = self.phi[1:]
-      else:
-        self.intercept_ = 'NA'
-        self.coef_ = self.phi
-      u = y - z @ self.phi
-      RSS = np.sum(u**2)
-      TSS = np.sum((y - np.mean(y))**2)
-      self.R2 = 1 - RSS/TSS
-      self.s2 = RSS/(z.shape[0] - z.shape[1])
-      self.SE = np.sqrt(self.s2 * np.diagonal(np.linalg.inv(z.T @ z)))
-      self.t = self.phi/self.SE
-      self.p = (1 - t.cdf(abs(self.t), df = z.shape[0] - z.shape[1]))*2
-
-    def predict(self, x):
-      if self.const ==1:
-        z = np.concatenate([np.ones((x.shape[0],1)), x], axis=1)
-      else:
-        z = x
-      fcst = z @ self.phi
-      return fcst.squeeze()
-
-    def summary(self):
-      """
-      summaryの出力
-      :return: summary dataframe
-      """
-      col_names = ["coef", "se", "t", "両側p値"]
-      output = pd.DataFrame(np.c_[self.phi, self.SE, self.t, self.p],
-                              index=self.index,
-                              columns=col_names)
+#sk-learn風の線形回帰分析クラス
+#SE, t-val,p-val, R2を出力
+#x, yをpandas DataFrameで入力、self.olsは(coef, SE, t値, p値)のDataFrame
+from scipy.stats import t
+import numpy as np 
+
+class linear_regression():
+    def __init__(self, fit_intercept = True):
+      self.const = fit_intercept
+
+    def fit(self, x, y):
+      if self.const ==1:
+        z = np.concatenate([np.ones((x.shape[0],1)), x], axis=1)
+        self.index = x.columns.insert(0,'const')
+      else:
+        z = x
+        self.index = x.columns
+      self.phi = np.linalg.inv(z.T @ z) @ (z.T @ y)
+      if self.const == 1:
+        self.intercept_ = self.phi[0]
+        self.coef_ = self.phi[1:]
+      else:
+        self.intercept_ = 'NA'
+        self.coef_ = self.phi
+      u = y - z @ self.phi
+      RSS = np.sum(u**2)
+      TSS = np.sum((y - np.mean(y))**2)
+      self.R2 = 1 - RSS/TSS
+      self.s2 = RSS/(z.shape[0] - z.shape[1])
+      self.SE = np.sqrt(self.s2 * np.diagonal(np.linalg.inv(z.T @ z)))
+      self.t = self.phi/self.SE
+      self.p = (1 - t.cdf(abs(self.t), df = z.shape[0] - z.shape[1]))*2
+
+    def predict(self, x):
+      if self.const ==1:
+        z = np.concatenate([np.ones((x.shape[0],1)), x], axis=1)
+      else:
+        z = x
+      fcst = z @ self.phi
+      return fcst.squeeze()
+
+    def summary(self):
+      """
+      summaryの出力
+      :return: summary dataframe
+      """
+      col_names = ["coef", "se", "t", "両側p値"]
+      output = pd.DataFrame(np.c_[self.phi, self.SE, self.t, self.p],
+                              index=self.index,
+                              columns=col_names)
       return output