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 = 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
