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jeroenboeye / higher_lower_optimizer.py
Last active Aug 31, 2020
Higher lower (simple card game) optimizer using epsilon greedy Monte Carlo learning. For educational purposes.
View higher_lower_optimizer.py
"""
Higher lower (simple card game) optimizer using epsilon greedy Monte Carlo learning. For educational purposes.
"""
from dataclasses import dataclass, field
from typing import List, Tuple
import numpy as np
@dataclass
class Player:
@jeroenboeye
jeroenboeye / blackjack_monte_carlo_optimizer.py
Last active Aug 31, 2020
Blackjack simulator where player policy is optimized using the Monte Carlo method. As described in Chapter 5(.3) of Reinforcement Learning, an introduction by Sutton and Barto
View blackjack_monte_carlo_optimizer.py
"""
Blackjack simulator where player policy is optimized using the Monte Carlo method.
As described in Chapter 5(.3) of Reinforcement Learning, an introduction by Sutton and Barto
"""
from dataclasses import dataclass, field
from typing import List, Tuple
import numpy as np
DECK = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10])
@jeroenboeye
jeroenboeye / blackjack_simple_strategy.py
Created Aug 21, 2020
Blackjack simulator where rewards of a fixed policy are calculated using Monte Carlo method. As described in Chapter 5(.1) of Reinforcement Learning, an introduction by Sutton and Barto
View blackjack_simple_strategy.py
"""
Blackjack simulator where rewards of a fixed policy are calculated using Monte Carlo method.
As described in Chapter 5(.1) of Reinforcement Learning, an introduction by Sutton and Barto
"""
from dataclasses import dataclass, field
from typing import List, Tuple
import numpy as np
DECK = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10])
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@jeroenboeye
jeroenboeye / recommender.py
Created Dec 12, 2019
Fast Numpy implementation of collaborative filtering
View recommender.py
import numpy as np
import pandas as pd
import sklearn.metrics.pairwise
def get_recommendation_matrix(listening_history, n_similar = 20):
"""Collaborative filtering using cosine similarity"""
# Get similarity matrix, shape = (n artists, n artists)
sim_matrix = sklearn.metrics.pairwise.cosine_similarity(listening_history.T)
# add miniscule noise for sorting without duplicate values
@jeroenboeye
jeroenboeye / pandas_utc_to_local_timestamp.py
Created Oct 9, 2019
Pandas UTC timestamp to local time based on timezone column
View pandas_utc_to_local_timestamp.py
import pandas as pd
# Example dataframe
tz_df = pd.DataFrame({'timestamp': pd.to_datetime(['2019-10-08 11:20:00+00:00',
'2019-10-08 01:20:00+00:00']),
'tz': ['cet', 'est']})
# Add local_time
tz_df['local_time'] = tz_df.apply(lambda x: x.timestamp.tz_convert(x.tz), axis=1)
print(tz_df)
@jeroenboeye
jeroenboeye / mosquito.py
Created Sep 30, 2018
Mosquito population model
View mosquito.py
import numpy as np
class Mosquito:
"""Contains the details of each Mosquito"""
def __init__(self, mother_gene_infected, father_gene_infected, sex):
self.genes = [mother_gene_infected, father_gene_infected]
self.sex = sex
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