alik604

Lambda 1 (DB to SQS)

import boto3
from boto3.dynamodb.conditions import Key
from boto3.dynamodb.types import TypeDeserializer

import json

print('Loading function')
tableName = 'fizzbuzz'

alik604

FFMPEG to reencode (encode) entire windows directory with GPU or x265

CPU

for %i in (*.mp4) do ffmpeg -i "%i" -c:v libx265 -vtag hvc1 -preset fast "%~ni_265.mp4"

ffmpeg -i input.mp4 -c:v libx265 -vtag hvc1 -preset fast input_compressed.mp4

Nvidia GPU

for %i in (*.mp4) do ffmpeg -i "%i" -c:v hevc_nvenc -preset fast "%~ni_nvda.mp4"

alik604

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import load_digits
from sklearn.decomposition import PCA, IncrementalPCA

iris = load_iris()
X = iris.data
y = iris.target

alik604

Python & Machine Learning Hello world

Quickly get up to speed

Software setup

Use Python 3.8.*. No point in 3.9, however it should be fine.

Run these to test if install is successful, and install some important packages. Please follow the errors as they come, one will ask the user to install C++ build tools, if not already installed (see below). This will take several minutes, as the dependencies are a few GBs. You must be off VPN, or set-proxy, as shown below.

alik604

import requests
import json
import webbrowser

USER = "alik604"
chrome_path = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s'
# Change to your OS - https://stackoverflow.com/a/24353812/5728614

req = requests.get('https://api.github.com/users/' + USER + '/starred?per_page=100')
# ?page=2&per_page=100 # page is not working, first result is always the same

alik604

import numpy as np
import pylab as pl
from numpy import fft


def fourierExtrapolation(x, n_predict):
    n = x.size
    n_harm = 10                     # number of harmonics in model
    t = np.arange(0, n)
    p = np.polyfit(t, x, 1)         # find linear trend in x

alik604

!pip install mnist
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd 
from sklearn import metrics
import mnist

# from hmmlearn.hmm import GaussianHMM, MultinomialHMM

X_train = mnist.train_images()

alik604

import urllib.parse as parse

url = ""
if 'url' not in globals():
    print("Enter URL to parse")
    url = input()


def printDict(data):
    for x in data:

alik604

# make dataset to input
def build_dataset(time_series, seq_length):
    dataX = []
    dataY = []
    for i in range(0, len(time_series) - seq_length):
        _x = time_series[i:i + seq_length, :]
        _y = time_series[i + seq_length, [-1]]  # Next close price
        print(_x, "->", _y)
        dataX.append(_x)
        dataY.append(_y)

alik604

# imports from matplotlib import pyplot as plt
from matplotlib import pyplot as plt
import pylab
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import proj3d
%matplotlib inline
%pylab inline

from sklearn.manifold import TSNE
from sklear.decomposition import PCA