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imohitmayank / Guide to Custom Recurrent Modeling in Keras.ipynb
Created November 14, 2020 09:33
Complimentary code for "Guide to Custom Recurrent Modeling in Keras" article by Mohit Mayank
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imohitmayank / node2vec_karateclub_eval.ipynb
Created August 4, 2020 16:26
To test the node2vec model of KarateClub on the LesMiserable graph (as reported in paper)
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imohitmayank / Multi-Arm bandit - Post.ipynb
Created September 17, 2019 18:25
Code for https://itnext.io/reinforcement-learning-with-multi-arm-bandit-decf442e02d2
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imohitmayank / bezier_curve.py
Created August 4, 2019 08:40
Draw bezier curve using Processing.py
# Libraries
import time
# Points
p = [{"x" : 100, "y" : 300}, {"x" : 500, "y" : 100}, {"x" : 700, "y" : 300}]
# default values
prev_x = p[0]['x']
prev_y = p[0]['y']
t = 0
@imohitmayank
imohitmayank / edit_distance.py
Created April 16, 2019 19:06
edit distance
import numpy as np
def editdistance(str1, str2):
# define
m, n = len(str1) + 1, len(str2) + 1
table = np.empty([m, n])
for i in range(m):
table[i, 0] = i
for j in range(n):
table[0, j] = j
for i in range(1, m):
@imohitmayank
imohitmayank / fibonaci_number_memo.py
Last active April 16, 2019 18:07
Fibonacci number - memoization
memory = {0: 0, 1: 1}
def fib(n):
if n in memory.keys():
return(memory[n])
else:
memory[n] = fib(n-1) + fib(n-2)
return memory[n]
@imohitmayank
imohitmayank / fibonaci_number.py
Created April 16, 2019 17:51
Fibonacci number
def fib(n):
if n == 0:
return 0
elif n == 1:
return 1
else:
return fib(n-1) + fib(n-2)
@imohitmayank
imohitmayank / miracle_worker_lp_results.py
Created September 13, 2018 15:06
# The status of the solution is printed to the screen
print("Status:", LpStatus[prob.status])
# Output=
# Status: Optimal
# Each of the variables is printed with it's resolved optimum value
for v in prob.variables():
print(v.name, "=", v.varValue)
# Output=
# Medicine_1_units = 3.0
@imohitmayank
imohitmayank / miracle_worker_lp.py
Last active October 27, 2018 14:21
Solving miracle worker using LP
# import PuLP
from pulp import *
# Create the 'prob' variable to contain the problem data
prob = LpProblem("The Miracle Worker", LpMaximize)
# Create problem variables
x=LpVariable("Medicine_1_units",0,None,LpInteger)
y=LpVariable("Medicine_2_units",0, None, LpInteger)
@imohitmayank
imohitmayank / nlu_output
Last active August 11, 2018 14:29
rasa_starter/nlu_output.json
{'intent': {'name': 'query_days_in_month', 'confidence': 0.6128492334410716}, 'entities': [{'start': 17, 'end': 24, 'value': 'january', 'entity': 'month', 'confidence': 0.5002208121139594, 'extractor': 'ner_crf'}], 'intent_ranking': [{'name': 'query_days_in_month', 'confidence': 0.6128492334410716}, {'name': 'bye', 'confidence': 0.1951941314517159}, {'name': 'greet', 'confidence': 0.19195663510721267}], 'text': 'How many days in January'}
{'intent': {'name': 'query_days_in_month', 'confidence': 0.6105606961005596}, 'entities': [{'start': 17, 'end': 22, 'value': 'march', 'entity': 'month', 'confidence': 0.5002208121139594, 'extractor': 'ner_crf'}], 'intent_ranking': [{'name': 'query_days_in_month', 'confidence': 0.6105606961005596}, {'name': 'bye', 'confidence': 0.20686031746694789}, {'name': 'greet', 'confidence': 0.18257898643249237}], 'text': 'How many days in March'