Update Linear.py

Linear.py

@@ -6,47 +6,67 @@ import numpy as np
 import pandas as pd
 
 class linear_regression():
-    def __init__(self, fit_intercept = True):
-      self.const = fit_intercept
+    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
+        # pandas DataFrame → numpy配列に変換
+        if isinstance(x, pd.DataFrame):
+            x_np = x.values
+            x_cols = x.columns
+        else:
+            x_np = x
+            x_cols = [f"x{i}" for i in range(x.shape[1])]
+        
+        # yがSeriesかDataFrameかに関係なく1列の配列にする
+        if isinstance(y, (pd.Series, pd.DataFrame)):
+            y_np = np.asarray(y).reshape(-1, 1)
+        else:
+            y_np = y.reshape(-1, 1)
+
+        if self.const:
+            ones = np.ones((x_np.shape[0], 1))
+            z = np.concatenate([ones, x_np], axis=1)
+            self.index = ['const'] + list(x_cols)
+        else:
+            z = x_np
+            self.index = list(x_cols)
+
+        self.phi = np.linalg.inv(z.T @ z) @ (z.T @ y_np)
+        if self.const:
+            self.intercept_ = self.phi[0, 0]
+            self.coef_ = self.phi[1:].flatten()
+        else:
+            self.intercept_ = 'NA'
+            self.coef_ = self.phi.flatten()
+
+        u = y_np - z @ self.phi
+        RSS = np.sum(u**2)
+        TSS = np.sum((y_np - np.mean(y_np))**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.flatten() / self.SE
+        self.p = (1 - t.cdf(np.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()
+        if isinstance(x, pd.DataFrame):
+            x_np = x.values
+        else:
+            x_np = x
+
+        if self.const:
+            z = np.concatenate([np.ones((x_np.shape[0], 1)), x_np], axis=1)
+        else:
+            z = x_np
+
+        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],
+        col_names = ["coef", "se", "t", "両側p値"]
+        output = pd.DataFrame(np.c_[self.phi.flatten(), self.SE, self.t, self.p],
                               index=self.index,
                               columns=col_names)
-      return output
+        print(f'決定係数R^2: {self.R2}')
+        return output