def ceshiLR(filename): # 加载数据 dataMat,labelMat = loadDataSet(filename) # 转换数据 dataArr = mat(dataMat) labelMat = mat(labelMat).transpose() weights = stocGradAscent2(dataArr,labelMat) plotBestFit(dataArr,labelMat,weights)
def standRegres(xArr, yArr): xMat = mat(xArr) yMat = mat(yArr).T xTx = xMat.T * xMat if np.linalg.det(xTx) == 0.0: print("This matrix is singular,cannot do inverse") return ws = xTx.T * (xMat.T * yMat) return ws
def regression1(filename): xArr, yArr = loadDataSet(filename) xMat = mat(xArr) yMat = mat(yArr) ws = standRegres(xArr, yArr) fig = plt.figure() ax = fig.add_subplot(111) ax.scatter(xMat[:, 1].flatten(), yMat[:, 0].flatten().A[0]) xCopy = xMat.sort(0) yHat = xCopy * ws ax.plot(xCopy[:, 1], yHat) plt.show()
def test_returntype(self): a = array([[0, 1], [0, 0]]) assert type(matrix_power(a, 2)) is ndarray a = mat(a) assert type(matrix_power(a, 2)) is matrix
def test_returntype(self): a = np.array([[0, 1], [0, 0]]) assert_(type(matrix_power(a, 2)) is np.ndarray) a = mat(a) assert_(type(matrix_power(a, 2)) is matrix)
def test_returntype(self): a = array([[0,1],[0,0]]) assert type(matrix_power(a, 2)) is ndarray a = mat(a) assert type(matrix_power(a, 2)) is matrix