def evqualitygrad(X, theta, ik, jk, angle_num, angle_index, dim, ndata):
V = gradU(theta, angle_index, ik, jk, dim)
U1 = build_Uab(theta, 0, angle_index-1, ik, jk, dim)
U2 = build_Uab(theta, angle_index+1, angle_num-1, ik, jk, dim)
A = buildA(X, U1, V, U2)
Y = rotate_givens(X, theta, ik, jk, angle_num, dim)
# Find max of each row
r_ndata = range(ndata)
Y_sq = Y**2
max_index = Y_sq.argmax(axis=1)
max_values = Y[r_ndata,max_index]
max_A_values = A[r_ndata,max_index]
mv_sq = max_values**2
mv_cb = max_values*mv_sq
A_x_Y = A*Y
# Compute gradient
dJ = sum_dJ(A_x_Y, Y_sq, mv_sq, mv_cb, max_A_values, dim, ndata)
return dJ
def evqualitygrad(X, theta, ik, jk, angle_num, angle_index, dim, ndata):
V = gradU(theta, angle_index, ik, jk, dim)
U1 = build_Uab(theta, 0, angle_index-1, ik, jk, dim)
U2 = build_Uab(theta, angle_index+1, angle_num-1, ik, jk, dim)
A = buildA(X, U1, V, U2)
Y = rotate_givens(X, theta, ik, jk, angle_num, dim)
# Find max of each row
r_ndata = range(ndata)
Y_sq = Y**2
max_index = Y_sq.argmax(axis=1)
max_values = Y[r_ndata,max_index]
max_A_values = A[r_ndata,max_index]
mv_sq = max_values**2
mv_cb = max_values*mv_sq
A_x_Y = A*Y
# Compute gradient
dJ = sum_dJ(A_x_Y, Y_sq, mv_sq, mv_cb, max_A_values, dim, ndata)
return dJ
def rotate_givens(X, theta, ik, jk, angle_num, dim):
G = build_Uab(theta, 0, angle_num-1, ik, jk, dim)
Y = X.dot(G)
return Y
def rotate_givens(X, theta, ik, jk, angle_num, dim):
G = build_Uab(theta, 0, angle_num-1, ik, jk, dim)
Y = X.dot(G)
return Y
