krinkere

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

So you're curious in learning this new thing called Reactive Programming, particularly its variant comprising of Rx, Bacon.js, RAC, and others.

Learning it is hard, even harder by the lack of good material. When I started, I tried looking for tutorials. I found only a handful of practical guides, but they just scratched the surface and never tackled the challenge of building the whole architecture around it. Library documentations often don't help when you're trying to understand some function. I mean, honestly, look at this:

Rx.Observable.prototype.flatMapLatest(selector, [thisArg])

Projects each element of an observable sequence into a new sequence of observable sequences by incorporating the element's index and then transforms an observable sequence of observable sequences into an observable sequence producing values only from the most recent observable sequence.

krinkere

"""
Example of adding a filter to a Logger's Handler.
"""
from logging import getLogger, FileHandler, INFO, ERROR


class LogOneLevel:
   """Enables messages at one specifie level only"""

   def __init__(self, level):

krinkere

import javax.net.ssl.SSLSocket;
import javax.net.ssl.SSLSocketFactory;
import java.io.*;

/** Establish a SSL connection to a host and port, writes a byte and
 * prints the response. See
 * http://confluence.atlassian.com/display/JIRA/Connecting+to+SSL+services
 */
public class SSLPoke {
    public static void main(String[] args) {

krinkere

from __future__ import division
from itertools import izip, count
import matplotlib.pyplot as plt
from numpy import linspace, loadtxt, ones, convolve
import numpy as np
import pandas as pd
import collections
from random import randint
from matplotlib import style
style.use('fivethirtyeight')

krinkere

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager
from sklearn import svm
 
# Generate train data
X = 0.3 * np.random.randn(100, 2)
X_train = np.r_[X + 2, X - 2]
# Generate some regular novel observations
X = 0.3 * np.random.randn(20, 2)

krinkere

import numpy

arr = [10, 386, 479, 627, 20, 523, 482, 483, 542, 699, 535, 617, 577, 471, 615, 583, 441, 562, 563, 527, 453, 530, 433, 541, 585, 704, 443, 569, 430, 637, 331, 511, 552, 496, 484, 566, 554, 472, 335, 440, 579, 341, 545, 615, 548, 604, 439, 556, 442, 461, 624, 611, 444, 578, 405, 487, 490, 496, 398, 512, 422, 455, 449, 432, 607, 679, 434, 597, 639, 565, 415, 486, 668, 414, 665, 763, 557, 304, 404, 454, 689, 610, 483, 441, 657, 590, 492, 476, 437, 483, 529, 363, 711, 543]

elements = numpy.array(arr)

mean = numpy.mean(elements, axis=0)
sd = numpy.std(elements, axis=0)

final_list = [x for x in arr if (x > mean - 2 * sd)]

krinkere

import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import matplotlib.font_manager

from sklearn import svm
from sklearn.covariance import EllipticEnvelope
from sklearn.ensemble import IsolationForest
from sklearn.neighbors import LocalOutlierFactor

krinkere

FROM nvidia/cuda:10.0-cudnn7-devel

RUN apt-get update \
  && apt-get install -y --no-install-recommends \
    build-essential \
    curl \
    git \
  && rm -rf /var/lib/apt/lists/*

RUN curl -qsSLkO \

krinkere

FROM nvidia/cuda:10.0-cudnn7-devel

RUN apt-get update \
  && apt-get install -y --no-install-recommends \
    build-essential \
    curl \
    git \
  && rm -rf /var/lib/apt/lists/*

RUN curl -qsSLkO \

krinkere

FROM nvidia/cuda:10.0-cudnn7-devel

RUN apt-get update \
  && apt-get install -y --no-install-recommends \
    build-essential \
    curl \
    git \
  && rm -rf /var/lib/apt/lists/*

RUN curl -qsSLkO \