khalido

# gets movie info from OpenaI

def get_movie_info(movie: str = None, debug=False):
  """returns movie info from openai as a dict"""
    msg = f"""You love movies and are helping complete a movie database. 
    Give me a short plot summary, main actors and concise review of the movie '{movie}'. Return the results in Json format with the fields: 
    ["summary", "review", "actors"]."""

    completion = client.chat.completions.create(
        model="gpt-3.5-turbo",

khalido

Install ublock origin from here - be wary of fake clones of it like ublock.org.

To block annoying accept these cookie popups:

ublock origin go to settings > filters list > annoyances, turn on easylist-cookies

khalido

"""
For most purposes, the pathlib lib replaces all the with open() file stuff.
"""

from pathlib import Path 

# to get the contents of a file
txt = Path(txt_file).read_text() # txt_file can be str or Path obj
bin_data = Path(binary_file).read_bytes() # binary_file can be str or Path obj

khalido

Get size of directories in a folder sorted by size:

du -h --max-depth=1 | sort -hr

List files with humun readable file sizes:

ls -lh

khalido

# add code here

khalido

# below code from
# https://alex.miller.im/posts/linear-model-custom-loss-function-regularization-python/

def mean_absolute_percentage_error(y_pred, y_true, sample_weights=None):
    """Mean absolute percentage error regression loss"""
    y_true = np.array(y_true)
    y_pred = np.array(y_pred)
    assert len(y_true) == len(y_pred)
    
    if np.any(y_true==0):

khalido

from collections import defaultdict
import numpy as np

inp1 = """.#..#
.....
#####
....#
...##"""

inp2 = """......#.#.

khalido

# see https://github.com/stared/livelossplot for live plots, or tensorboard
# but for simple stuff the below is good enough

def plot_history(history, log=True):
    """takes in a keras history object and plots train and val loss and accuracy"""
    
    # dict which stores train & val accuracy and losses over epochs
    hist = history.history
    
    fig, (ax, ax2) = plt.subplots(1,2, figsize=(13,6))

khalido

from sklearn.model_selection import train_test_split
from sklearn import metrics # for evaluation
from sklearn.ensemble import RandomForestClassifier

# initiate a classifier and train on some data
rf = RandomForestClassifier(n_jobs=-1)
rf.fit(x_train, y_train)

# predict
y_predict = rf.predict(x_train)

khalido

# make a list of colours
cmap = colors.ListedColormap(['red','white','black'])

# define a range of numbers: -0.5-0.5, 0.5-1.5, 1.5-2.5
bounds=[-0.5, 0.5, 1.5, 2.5] 

# Generate a colormap index based on discrete intervals.
norm = colors.BoundaryNorm(bounds, cmap.N)

# plot