[Numpy-discussion] Improvement of performance

[gerardob]

Hello, I have written a very simple code that computes the gradient by finite
differences of any general function. Keeping the same idea, I would like
modify the code using numpy to make it faster.
Any ideas?
Thanks.

       def grad_finite_dif(self,x,user_data = None):		
		assert len(x) == self.number_variables
		points=[]
		for j in range(self.number_variables):
			points.append(x.copy())
			points[len(points)-1][j]=points[len(points)-1][j]+0.0000001		
		delta_f = []
		counter=0
		for j in range(self.number_variables):
			delta_f.append((self.eval(points[counter])-self.eval(x))/0.0000001)
			counter = counter + 1				
		return array(delta_f)
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