There has been some related questions for over 7 years, but I raise this issue again as I could see no 'numpy' way iteration method provided.
The task is as follows: If I have an numpy array 'arr' and have a custom function 'fn', how could I iteratively apply 'fn' over the 'arr'? 'fn' cannot be constructed by ufunc tools.
Below is the toy_code I come up with this:
import numpy as np
r_list = np.arange(1,6,dtype=np.float32)
# r_list = [1. 2. 3. 4. 5.]
r_list_extended = np.append([0.],r_list)
R_list_extended = np.zeros_like(r_list_extended)
print(r_list)
gamma = 0.99
pv_mc = lambda a, x: x+ a*gamma
# no cumsum, accumulate available
for i in range(len(r_list_extended)):
if i ==0: continue
else: R_list_extended[i] = pv_mc(R_list_extended[i-1],r_list_extended[i])
R_list = R_list_extended[1:]
print(R_list)
# R_list == [ 1. 2.99 5.9601 9.900499 14.80149401]
r_list is an array of r for each time. R_list is a cumulative sum of discounted r. Assume r_list and R_list are reverted beforehand. The loop in above does R[t] : r[t] + gamma * R[t-1]
I do not think this is the best way to utilize numpy.... If one can utilize tensorflow, then tf.scan() does the job as below:
import numpy as np
import tensorflow as tf
r_list = np.arange(1,6,dtype=np.float32)
# r_list = [1. 2. 3. 4. 5.]
gamma = 0.99
pv_mc = lambda a, x: x+ a*gamma
R_list_graph = tf.scan(pv_mc, r_list, initializer=np.array(0,dtype=np.float32))
with tf.Session() as sess:
R_list = sess.run(R_list_graph, feed_dict={})
print(R_list)
# R_list = [ 1. 2.99 5.9601 9.900499 14.801495]
Thanks in advance for your help!
You could use np.frompyfunc, whose documentation is somewhat obscure.
import numpy as np
r_list = np.arange(1,6,dtype=np.float32)
# r_list = [1. 2. 3. 4. 5.]
r_list_extended = np.append([0.],r_list)
R_list_extended = np.zeros_like(r_list_extended)
print(r_list)
gamma = 0.99
pv_mc = lambda a, x: x+ a*gamma
ufunc = np.frompyfunc(pv_mc, 2, 1)
R_list = ufunc.accumulate(r_list, dtype=np.object).astype(float)
print(R_list)