diff --git a/MultiView/Run_TwoCam_FNSRansac.py b/MultiView/Run_TwoCam_FNSRansac.py
deleted file mode 100644
index 5c2aead..0000000
--- a/MultiView/Run_TwoCam_FNSRansac.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import matplotlib.pyplot as plt
-import csv
-import numpy as np
-from numpy.linalg import eigh
-import math
-from Common import ReadTwoCamPoints, Plot, BuildXi_F, BuildV0_F, BuildM_LS, BuildL, Epipolar_Constraint_Error, ThetaToF
-from Ransac import Ransac, RegresserBase
-from FNSTwoCamRegressor import FNSTwoCamRegressor
-
-
-# 最小二乗法で最適解を求めます。
-def TwoCam_LeastSquare(x0_list, y0_list, x1_list, y1_list, f0):
-    xi_list = BuildXi_F(x0_list, y0_list, x1_list, y1_list, f0)
-    M = BuildM_LS(xi_list)
-    # Mの最小固有値に対応する固有ベクトルthetaを得る。
-    _, eig_vec=eigh(M)
-    theta = eig_vec[:, 0].reshape(9)
-    theta = np.vstack(theta)
-
-    # スケールしても式の内容は変わらない。定数項が-1になるようにする。
-    theta /= -theta[8, 0]
-    return theta
-
-def main():
-    f0=1
-    MaxIter=100
-    ConvEPS=1e-5
-    SampleCount=9 # smallest point count to solve
-
-    x0_list, y0_list, x1_list, y1_list = ReadTwoCamPoints('twoCamPoints.csv')
-    N=x0_list.shape[0]
-    assert N == y0_list.shape[0]
-    assert N == x1_list.shape[0]
-    assert N == y1_list.shape[0]
-
-    theta = TwoCam_LeastSquare(x0_list, y0_list, x1_list, y1_list, f0)
-    #print(theta)
-    F = ThetaToF(theta)
-    print(f"F={F}")
-    err = Epipolar_Constraint_Error(x0_list, y0_list, x1_list, y1_list, f0, F)
-    print(f"Epipolar constraint error = {err}")
-
-
-if __name__ == "__main__":
-    main()
-
-
-
-