def precision_calc(resample: np.matrix):
"""
Calculate precision score from matrix. Assume the first column to be y_true and the second column to be y_pred
Args:
resample: matrix with 2 columns [y_true, y_pred]
Returns:
precision score: float
"""
return precision_score(resample.tolist()[0], resample.tolist()[1])
def multiply_vecmat(vec: vec3d, mat: np.matrix) -> vec3d: if mat.shape[0] == 1: mat=mat.tolist()[0] else: mat=mat.tolist() return vec3d( vec.x*mat[0][0]+vec.y*mat[1][0]+vec.z*mat[2][0]+vec.w*mat[3][0], vec.x*mat[0][1]+vec.y*mat[1][1]+vec.z*mat[2][1]+vec.w*mat[3][1], vec.x*mat[0][2]+vec.y*mat[1][2]+vec.z*mat[2][2]+vec.w*mat[3][2], vec.x*mat[0][3]+vec.y*mat[1][3]+vec.z*mat[2][3]+vec.w*mat[3][3])
def get_r_squared(model: Linear_Regression, X: np.matrix, y: np.matrix) -> float:
nominator: float = 0
denominator: float = 0
y_list: List[List[float]] = y.tolist()
y_avg: float = np.average(y_list)
for idx, x in enumerate(X.tolist()):
nominator += (model.fit(x[:4]) - y_list[idx][0]) ** 2
denominator += (y_avg - y_list[idx][0]) ** 2
return 1 - (nominator/denominator)
def __plot_principal_components__(features: np.ndarray, s: np.matrix = None):
plt.scatter(features[:, :1], features[:, 1:])
if s is not None:
s = s.transpose()
s_list = s.tolist()
x = np.linspace(-2, 2, 100)
m1 = s_list[0][1] / s_list[0][0]
y1 = m1 * x
m2 = s_list[1][1] / s_list[1][0]
y2 = m2 * x
plt.plot(x, y1, c='blue')
plt.plot(x, y2, c='red')
plt.show()
def matrixAdjugate(mat: np.matrix) -> np.matrix:
"""
求伴随
:param mat:
:return:
"""
arr = mat.tolist()
row = len(arr)
col = len(arr[0])
if row != col:
raise Exception("row != col")
retArr = np.arange(row * col).reshape(row, col)
for i in range(0, row):
for j in range(0, col):
retArr[j][i] = ModuloUtil.matrixAlgebraicComplementDet(
mat, i, j)
ret = np.mat(retArr)
return ret
def matrixModuloInverse(mat: np.matrix, modulo: int) -> np.matrix:
"""
矩阵模逆
:param mat:
:param modulo:
:return:
"""
adjugateMatrix = ModuloUtil.matrixAdjugate(mat)
det = ModuloUtil.matrixDet(mat)
if det == 0:
raise Exception("matrix det is 0, has not Inverse!")
accArr = adjugateMatrix.tolist()
arr = mat.tolist()
row = len(arr)
col = len(arr[0])
retMatArr = np.arange(row * col).reshape(row, col)
for i in range(0, row):
for j in range(0, col):
retMatArr[i][j] = ModuloUtil.numFractionModulo(
int(accArr[i][j]), det, modulo)
ret = np.mat(retMatArr)
return ret
def unit_to_scalar(unit_matrix: np.matrix):
return unit_matrix.tolist()[0][0]
def calcResidual(theta: np.matrix):
c = np.matrix([1.0/math.tan(*value) for value in theta.tolist()]).T
A = makeA(*c.T.tolist())
omega = makeOmega(*theta.T.tolist())
C = makeC(*c.T.tolist())
return A * (C ** -1) * omega
def matrix2Array(self, mat: np.matrix) -> np.ndarray:
return np.concatenate(mat.tolist())