jeroenboeye

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

jeroenboeye

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

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

Keybase proof

I hereby claim:

• I am jeroenboeye on github.
• I am jeroenboeye (https://keybase.io/jeroenboeye) on keybase.
• I have a public key ASAamCw_-jZZz1EENFIDcZ4Vs5rfudU85q_2MvURs_3Aewo

To claim this, I am signing this object:

jeroenboeye

"""
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])

jeroenboeye

"""
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

"""
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

import glob
from PIL import Image

# Measure current aspect ratio beforehand
width = 700  # desired width
aspect_ratio = 0.64267  # original aspect ratio (width / height)
height = int(width / aspect_ratio)  # derived new height

# Read png images from plots folder, resize them and add to list. 
# First list element is saved as img, rest as imgs (list)

jeroenboeye

from __future__ import annotations

from dataclasses import dataclass
from typing import Optional


@dataclass
class Node:
    d: int
    x: int

jeroenboeye

repos:
  - repo: local
    hooks:
      - id: avoid-excel-files
        name: Check for Excel files
        entry: Excel files should not be committed to the repo
        language: fail
        files: \.(xls|xlsx|xlsm)$
        description: 'Fails on Excel files, see: https://pre-commit.com/#fail'