sithu

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

learning_rate = 0.01
training_epochs = 1000
num_labels = 3
batch_size = 100

x1_label0 = np.random.normal(1, 1, (100, 1))

sithu

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

learning_rate = 0.1
training_epochs = 2000


def sigmoid(x):
    return 1. / (1. + np.exp(-x))

sithu

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

learning_rate = 0.01
training_epochs = 1000

x1 = np.random.normal(-4, 2, 1000)
x2 = np.random.normal(4, 2, 1000)
xs = np.append(x1, x2)

sithu

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

#  1-dimensional values. 
# Numbers close to 5 will be given the label [0], and 
# Numbers close to 2 will be given the label [1].

x_label0 = np.random.normal(5, 1, 10)
# outliers: 

sithu

import math

size = 5
# ( 1 + (1/1!) + (1/2!) + (1/3!) + (1/4!) + (1/5!) + ......... + 1/i! )
_list = [(1 / math.factorial(i)) for i in range(size)]
e = sum(_list) 

print(f"e={e}")

sithu

import sys

# Prompt the user to enter the number of students
numOfStudents = sys.stdin.readline()
print("number of students = " + numOfStudents)
maxScore = 0
topStudent = None

for i in range(int(numOfStudents)):
    line = sys.stdin.readline()

sithu

A Typical directory structure for running tests using unittest

Ref : stackoverflow

The best solution in my opinion is to use the unittest [command line interface][1] which will add the directory to the sys.path so you don't have to (done in the TestLoader class).

For example for a directory structure like this:

new_project

├── antigravity.py

sithu

const print = console.log;

const actions = [
    { type: 'ADD', value: 1 }, 
    { type: 'SUBTRACT', value: 2 },
    { type: 'ADD', value: 3 }, 
    { type: 'SUBTRACT', value: 4 },
    { type: 'ADD', value: 5 },
]

sithu

import io.netty.bootstrap.Bootstrap
import io.netty.channel.ChannelHandlerContext
import io.netty.channel.SimpleChannelInboundHandler
import io.netty.channel.nio.NioEventLoopGroup
import io.netty.channel.socket.nio.NioDatagramChannel
import io.netty.util.CharsetUtil
import io.netty.util.concurrent.DefaultThreadFactory
import kotlinx.coroutines.experimental.async
import kotlinx.coroutines.experimental.newFixedThreadPoolContext

sithu

# docker build -t ubuntu1604py36
FROM ubuntu:16.04

RUN apt-get update
RUN apt-get install -y software-properties-common vim
RUN add-apt-repository ppa:jonathonf/python-3.6
RUN apt-get update

RUN apt-get install -y build-essential python3.6 python3.6-dev python3-pip python3.6-venv
RUN apt-get install -y git