def compute_x_ks(points, k_function):
x_ks = []
t = points[1]
for point in points:
x = np.subtract(point, t)
#print len(k_function(point))
x_ks.append(np.subtract(x, k_function(point)))
t = point
return x_ks
def compute_x_ks(points, k_function):
x_ks = []
t = points[1]
for point in points:
x=np.subtract(point, t)
#print len(k_function(point))
x_ks.append(np.subtract(x, k_function(point)))
t = point
return x_ks
def gradient_function(point, k_function, g_function):
dimension = len(point)
gradient = np.zeros(dimension)
k_vector = k_function(point)
g_matrix = g_function(point)
# print len(k_function(point))
#m,n=g_matrix.shape
for i in range(dimension):
gradient[i] = k_vector[i]
for j in range(3):
gradient[i] += math.sqrt(
2.0 * 0.0001) * g_matrix[j][i] * np.random.normal(0, 0.001)
return gradient
def gradient_function(point,k_function,g_function):
dimension = len(point)
gradient = np.zeros(dimension)
k_vector = k_function(point)
g_matrix = g_function(point)
# print len(k_function(point))
#m,n=g_matrix.shape
for i in range(dimension):
gradient[i] = k_vector[i]
for j in range(3):
gradient[i] += math.sqrt(2.0*0.0001)*g_matrix[j][i]*np.random.normal(0, 0.001)
return gradient