timeit.timeit('a = range(50);b=range(25, 75);set(a)&set(b)') # takes about 5 seconds timeit.timeit('a = range(50);b=range(25, 75);[x for x in a if x in b]') # takes about 15 seconds
Saturday, June 10, 2017
Snippet: List Comprehension vs Set AND operation
Sunday, April 30, 2017
Interesting Plot Snippet: Bitwise Or and Addition Operation
If the sum and the result of bitwise or operation of two numbers are the same, plotting these numbers in a scatter diagram produces a beautiful shape.
import matplotlib.pyplot as plt l1 = [] l2 = [] for i in xrange(1000): for j in xrange(1000): if (i + j) == (i | j): l1.append(i) l2.append(j) plt.scatter(l1,l2) plt.show()This is the figure that comes up:
Monday, January 9, 2017
Value Iteration in Gridworld, Reinforcement Learning
import operator import copy import math class State: def __init__(self, rowid, colid): self.row = rowid self.col = colid def __str__(self): return str(self.row) + " " + str(self.col) def get_value_from_state(state): return state_values[state.row][state.col] def get_reward_from_state(state): return reward_values[state.row][state.col] def move_to_new_state(state, action): if action == 'left': # state will only change columnwise if state.col == 0 or (state.col == 2 and state.row == 1): # then we can't move left return copy.deepcopy(state) else: return State(state.row, (state.col - 1)) if action == 'right': # state will only change columnwise if state.col == 3 or ( state.col == 0 and state.row == 1): # can't move right when on the right boundary or at the first column return copy.deepcopy(state) else: return State(state.row, (state.col + 1)) if action == 'up': if state.row == 0 or (state.row == 2 and state.col == 1): return copy.deepcopy(state) else: return State(state.row - 1, state.col) if action == 'down': if state.row == 2 or (state.row == 0 and state.col == 1): return copy.deepcopy(state) else: return State(state.row + 1, state.col) def moveWithProbabilites(state, action): if action == 'left' or action == 'right': thisstate = move_to_new_state(state, action) upstate = move_to_new_state(state, 'up') downstate = move_to_new_state(state, 'down') return {thisstate: .8, upstate: .1, downstate: .1} elif action == 'up' or action == 'down': thisstate = move_to_new_state(state, action) leftstate = move_to_new_state(state, 'left') rightstate = move_to_new_state(state, 'right') return {thisstate: .8, leftstate: .1, rightstate: .1} state_values = [ [0, 0, 0, 1], [0, -100, 0, -1], # -100 means we can't go this way [0, 0, 0, 0] ] reward_values = [ # in each state, the agent gets a reward of -.03 [-.03, -.03, -.03, -.03], [-.03, -.03, -.03, -.03], [-.03, -.03, -.03, -.03] ] gamma = 1 states = [ State(0, 0), State(0, 1), State(0, 2), State(1, 0), State(1, 2), State(2, 0), State(2, 1), State(2, 2), State(2, 3), ] actions = ['left', 'right', 'up', 'down'] prev = state_values[0][0] if __name__ == "__main__": while True: for s in states: mx = -9999999 for a in actions: dic = moveWithProbabilites(s, a) tempSum = get_reward_from_state(s) # to be added to the values later tempSum += gamma * dic.items()[0][1] * get_value_from_state(dic.items()[0][0]) # .9 * .1 * v(s) tempSum += gamma * dic.items()[1][1] * get_value_from_state(dic.items()[1][0]) tempSum += gamma * dic.items()[2][1] * get_value_from_state(dic.items()[2][0]) if tempSum > mx: mx = tempSum state_values[s.row][s.col] = mx if abs(state_values[0][0] - prev) < .00003: break prev = state_values[0][0] print state_values
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