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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# https://gist.github.com/sithu/4722649d23c83440f2067ed429fa434b
import utils
from sklearn.svm import SVC

# Loading the linear dataset
# linear.csv: https://gist.github.com/sithu/701d1182d63b01e740bb244d8059ceb1
linear_data = pd.read_csv('linear.csv')

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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))

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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))

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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)

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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: 

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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}")

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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()

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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