This question is originally presented here https://github.com/RobotLocomotion/drake/issues/12484. the question is about how to use the function.
the function description: https://drake.mit.edu/pydrake/pydrake.forwarddiff.html#pydrake.forwarddiff.jacobian
my code: '''
from pydrake.forwarddiff import jacobian
import math
import numpy as np
def f(t):
return np.array([1.5 - 0.5 * sigmoid(t - 2.5), 0.5, 0.0])
print jacobian(f,0.)
''' The returned error info:
AttributeError Traceback (most recent call last)
in ()
48
49
---> 50 print jacobian(f,0.)
51 #print jacobian(f,np.array[0.])
52
/drake/lib/python2.7/site-packages/pydrake/forwarddiff.py in jacobian(function, x)
38 The function should be vector-input and vector-output.
39 """
---> 40 x_ad = np.empty(x.shape, dtype=np.object)
41 for i in range(x.size):
42 der = np.zeros(x.size)
AttributeError: 'float' object has no attribute 'shape'
If I changed that line into
print jacobian(f,np.array[0.])
then the error becomes:
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
TypeError Traceback (most recent call last)
in ()
49
50 #print jacobian(f,0.)
---> 51 print jacobian(f,np.array[0.])
52
53
TypeError: 'builtin_function_or_method' object has no attribute 'getitem'
If I changed that line into:
print jacobian(f,[0.])
the error:
AttributeError Traceback (most recent call last)
in ()
50 #print jacobian(f,0.)
51 #print jacobian(f,np.array[0.])
---> 52 print jacobian(f,[0.])
53
54 # Run the simulation. Parameters not described above
/drake/lib/python2.7/site-packages/pydrake/forwarddiff.py in jacobian(function, x)
38 The function should be vector-input and vector-output.
39 """
---> 40 x_ad = np.empty(x.shape, dtype=np.object)
41 for i in range(x.size):
42 der = np.zeros(x.size)
AttributeError: 'list' object has no attribute 'shape'
So the question is, how should I use it correctly? Thanks
*****************update line*****************************************
Thanks to Eric's finding. I change the code into: '''
from pydrake.forwarddiff import jacobian
import math
import numpy as np
def f(t):
return np.array([1.5 - 0.5 *(t - 2.5)])
def g(t):
return np.array([1.5 - 0.5 *(t - 2.5), 0.5, 0.0])
x=0.
x = np.asarray(x)
print jacobian(f,x) # accepted
print jacobian(f,0.) # not accepted
print jacobian(g,x) # not accepted
''' The result output is: '''
[[-0.5]]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-15-1b94cb728eed> in <module>()
9 x = np.asarray(x)
10 print jacobian(f,x)
---> 11 print jacobian(f,0.)
/drake/lib/python2.7/site-packages/pydrake/forwarddiff.py in jacobian(function, x)
38 The function should be vector-input and vector-output.
39 """
---> 40 x_ad = np.empty(x.shape, dtype=np.object)
41 for i in range(x.size):
42 der = np.zeros(x.size)
AttributeError: 'float' object has no attribute 'shape'
''' SO, THERE ARE TWO PROBLEMS HERE AT LEAST: 1. whenever i want to use print jacobian(f,0.), I have to transfer the float constant into np.asarray first. This is awkward. 2. Notice the jacobian of g(t), the error of that line is: ''' --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in () 11 print jacobian(f,x) # accepted 12 #print jacobian(f,0.) # not accepted ---> 13 print jacobian(g,x) # not accepted
/drake/lib/python2.7/site-packages/pydrake/forwarddiff.py in jacobian(function, x)
45 y_ad = function(x_ad)
46 return np.vstack(
---> 47 [y.derivatives() for y in y_ad.flat]).reshape(y_ad.shape + (-1,))
48
49
AttributeError: 'float' object has no attribute 'derivatives'
''' If i'm guessing right, one has to change everything in g(x) to be np.asarray. This is awkward...
