Why is IndexError thrown when I try to index a numpy ndarray with another array?-CodePudding

I have a 5 dimensional ndarray called self.q_table. I've got a regular array, the length of which is 4. When I try to find out the maximum value in that row like this...

max = np.max(self.q_table[regular_array])

...I get an IndexError, even though the elements of regular_array are smaller than the dimensions of q_table.

I tried to alter the dimensions of both arrays, but it didn't get better.

Edit:

The error is IndexError: index 11 is out of bounds for axis 0 with size 10

Numpy is imported as np, and the last line in this sample throws the error.

class AgentBase:
def __init__(self, observation_space):
    OBSERVATION_SPACE_RESOLUTION = [10, 15, 15, 15]
    self.q_table = np.zeros([*OBSERVATION_SPACE_RESOLUTION, 4])
    max_val = np.max(self.q_table[self.quantize_state(observation_space, [-150, 100, 3, 3])])
    print(max_val)

@staticmethod
def quantize_state(observation_space, state):
    state_quantized = np.zeros(len(state))
    lin_spaces = []
    for i in range(len(observation_space)):
        lin_spaces.append(np.linspace(observation_space[i][0], observation_space[i][1],
                                     OBSERVATION_SPACE_RESOLUTION[i] - 1, dtype=int))
    for i in range(len(lin_spaces)):
        state_quantized[i] = np.digitize(state[i], lin_spaces[i])
    return state_quantized.astype(int)

self.observation_space is a parameter with the following values:

CodePudding user response：

When you index an ndarray with a list (or, to quote the numpy docs, "a non-tuple sequence object"), you invoke advanced indexing, so when you index with [3,7,11,11] it tries to apply all those values to the first dimension.

I agree with @hpaulj’s comment that you may want to index with a tuple instead.