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import chumpy as ch
[x1, x2, x3] = ch.array(10), ch.array(20), ch.array(30)
result = x1+x2+x3
print result # prints [ 60.]
print result.dr_wrt(x1) # prints 1
scalaraP
import chumpy as ch
[x1, x2, x3] = ch.array(10), ch.array(20), ch.array(30)
for i in range(3): x2 = x1 + x2 + x3
x2.dr_wrt(x1) # pull cache
x2.dr_wrt(x3) # pull cache
x1.label='x1' # for clarity in show_tree()
x2.label='x2' # for clarity in show_tree()
x3.label='x3' # for clarity in show_tree()
x2.show_tree(cachelim=1e-4) # in MB
show_treez
import chumpy as ch
x1, x2, x3, x4 = ch.eye(10), ch.array(1), ch.array(5), ch.array(10)
y = x1*(x2-x3)+x4
print y
print y.dr_wrt(x2)
matrixz
import chumpy as ch
m = [ch.random.randn(100).reshape((10,10)) for i in range(3)]
y = m[0].dot(m[1]).dot(ch.linalg.inv(m[2])) * ch.linalg.det(m[0])
print y.shape
print y.dr_wrt(m[0]).shape
linalga
import chumpy as ch
import numpy as np
class Sin(ch.Ch):
dterms = ('x',)
def compute_r(self):
return np.sin(self.x.r)
def compute_dr_wrt(self, wrt):
import scipy.sparse
if wrt is self.x:
result = np.cos(self.x.r)
return scipy.sparse.diags([result.ravel()], [0]) if len(result)>1 else np.atleast_2d(result)
x1 = Ch([10,20,30])
result = Sin(x1) # or "result = Sin(x=x1)"
print result.r
print result.dr_wrt(x1)
inheritancea)
import chumpy as ch
x = ch.zeros(10)
y = ch.zeros(10)
# Beale's function
e1 = 1.5 - x + x*y
e2 = 2.25 - x + x*(y**2)
e3 = 2.625 - x + x*(y**3)
objective = {'e1': e1, 'e2': e2, 'e3': e3}
ch.minimize(objective, x0=[x,y], method='dogleg')
print x # should be all 3.0
print y # should be all 0.5
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