I defined a function
def softthresh(u, LAMBDA):
if np.fabs(u) <= LAMBDA:
return 0
else:
return ((np.fabs(u) - LAMBDA) * u / np.fabs(u))
u is a numpy array, and np.fabs will check the relations for each array element (np.fabs(u_i)). It gives me the following error:
The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
Follow up Question:
Strange behaviour in simple function.
def softthresh(u,LAMBDA):
for i in u:
if np.fabs(i)<=LAMBDA:
return 0
else:
return ((np.fabs(i)-LAMBDA)*u/np.fabs(i))
ll = 5.0
xx = np.arange(-10,11)
yy = softthresh(xx,ll)
What I get is not what I expect. for u (=xx ) array-elements that are smaller than 5 i should get zero. But i don't. Why?
You are calling return from inside the inner loop. Therefore, your function returns just after it evaluates the first member of u.
Since you are using NumPy, you should take advantage of NumPy's ability to operate on the whole array at once, and also of NumPy's smart indexing.
def softthreshold(u, LAMBDA):
notzero = np.fabs(u) > LAMBDA # find the indeces of elements that need to be scaled
rr = np.zeros_like(u) # an array the same size/type as u, already initialized to 0
rr[notzero] = (np.fabs(u[notzero])-LAMBDA)*u[notzero]/np.fabs(u[notzero]) # scale each of the members that aren't zero
return rr
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