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Remove code with side effects from main (#1577)

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* Remove code with side effects from main

When running tests withy pytest, some modules execute code in main scope
and open plot or browser windows.

Moves such code under `if __name__ == "__main__"`.

* fixup! Format Python code with psf/black push
This commit is contained in:
Mantas Zimnickas 2019-11-17 20:38:48 +02:00 committed by Christian Clauss
parent 5616fa9e62
commit 12f69a86f5
4 changed files with 516 additions and 505 deletions
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150
fuzzy_logic/fuzzy_operations.py
View File

@ -6,97 +6,97 @@ Requirements:
Python:
- 3.5
"""
# Create universe of discourse in python using linspace ()
import numpy as np
X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function (trapmf(), gbellmf(),gaussmf(), etc).
import skfuzzy as fuzz
abc1 = [0, 25, 50]
abc2 = [25, 50, 75]
young = fuzz.membership.trimf(X, abc1)
middle_aged = fuzz.membership.trimf(X, abc2)
# Compute the different operations using inbuilt functions.
one = np.ones(75)
zero = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
union = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
complement_a = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
alg_sum = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
alg_product = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
if __name__ == "__main__":
# Create universe of discourse in python using linspace ()
X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# max-min composition
# max-product composition
# Create two fuzzy sets by defining any membership function (trapmf(), gbellmf(),gaussmf(), etc).
abc1 = [0, 25, 50]
abc2 = [25, 50, 75]
young = fuzz.membership.trimf(X, abc1)
middle_aged = fuzz.membership.trimf(X, abc2)
# Compute the different operations using inbuilt functions.
one = np.ones(75)
zero = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
union = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
complement_a = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
alg_sum = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
alg_product = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# Plot each set A, set B and each operation result using plot() and subplot().
import matplotlib.pyplot as plt
# max-min composition
# max-product composition
plt.figure()
# Plot each set A, set B and each operation result using plot() and subplot().
import matplotlib.pyplot as plt
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("Young")
plt.grid(True)
plt.figure()
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("Middle aged")
plt.grid(True)
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("Young")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("union")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("Middle aged")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("intersection")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("union")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("complement_a")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("intersection")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("difference a/b")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("complement_a")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("alg_sum")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("difference a/b")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("alg_product")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("alg_sum")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("bdd_sum")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("alg_product")
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("bdd_difference")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("bdd_sum")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("bdd_difference")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()

9
machine_learning/polymonial_regression.py
View File

@ -36,8 +36,9 @@ def viz_polymonial():
return
viz_polymonial()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003

182
neural_network/gan.py
View File

@ -59,125 +59,133 @@ def plot(samples):
return fig
# 1. Load Data and declare hyper
print("--------- Load Data ----------")
mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
temp = mnist.test
images, labels = temp.images, temp.labels
images, labels = shuffle(np.asarray(images), np.asarray(labels))
num_epoch = 10
learing_rate = 0.00009
G_input = 100
hidden_input, hidden_input2, hidden_input3 = 128, 256, 346
hidden_input4, hidden_input5, hidden_input6 = 480, 560, 686
if __name__ == "__main__":
# 1. Load Data and declare hyper
print("--------- Load Data ----------")
mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
temp = mnist.test
images, labels = temp.images, temp.labels
images, labels = shuffle(np.asarray(images), np.asarray(labels))
num_epoch = 10
learing_rate = 0.00009
G_input = 100
hidden_input, hidden_input2, hidden_input3 = 128, 256, 346
hidden_input4, hidden_input5, hidden_input6 = 480, 560, 686
print("--------- Declare Hyper Parameters ----------")
# 2. Declare Weights
D_W1 = (
np.random.normal(size=(784, hidden_input), scale=(1.0 / np.sqrt(784 / 2.0))) * 0.002
)
# D_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
D_b1 = np.zeros(hidden_input)
D_W2 = (
np.random.normal(size=(hidden_input, 1), scale=(1.0 / np.sqrt(hidden_input / 2.0)))
print("--------- Declare Hyper Parameters ----------")
# 2. Declare Weights
D_W1 = (
np.random.normal(size=(784, hidden_input), scale=(1.0 / np.sqrt(784 / 2.0)))
* 0.002
)
# D_b2 = np.random.normal(size=(1),scale=(1. / np.sqrt(1 / 2.))) *0.002
D_b2 = np.zeros(1)
)
# D_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
D_b1 = np.zeros(hidden_input)
G_W1 = (
np.random.normal(size=(G_input, hidden_input), scale=(1.0 / np.sqrt(G_input / 2.0)))
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b1 = np.zeros(hidden_input)
G_W2 = (
D_W2 = (
np.random.normal(
size=(hidden_input, hidden_input2), scale=(1.0 / np.sqrt(hidden_input / 2.0))
size=(hidden_input, 1), scale=(1.0 / np.sqrt(hidden_input / 2.0))
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b2 = np.zeros(hidden_input2)
)
# D_b2 = np.random.normal(size=(1),scale=(1. / np.sqrt(1 / 2.))) *0.002
D_b2 = np.zeros(1)
G_W3 = (
G_W1 = (
np.random.normal(
size=(hidden_input2, hidden_input3), scale=(1.0 / np.sqrt(hidden_input2 / 2.0))
size=(G_input, hidden_input), scale=(1.0 / np.sqrt(G_input / 2.0))
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b3 = np.zeros(hidden_input3)
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b1 = np.zeros(hidden_input)
G_W4 = (
G_W2 = (
np.random.normal(
size=(hidden_input3, hidden_input4), scale=(1.0 / np.sqrt(hidden_input3 / 2.0))
size=(hidden_input, hidden_input2),
scale=(1.0 / np.sqrt(hidden_input / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b4 = np.zeros(hidden_input4)
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b2 = np.zeros(hidden_input2)
G_W5 = (
G_W3 = (
np.random.normal(
size=(hidden_input4, hidden_input5), scale=(1.0 / np.sqrt(hidden_input4 / 2.0))
size=(hidden_input2, hidden_input3),
scale=(1.0 / np.sqrt(hidden_input2 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b5 = np.zeros(hidden_input5)
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b3 = np.zeros(hidden_input3)
G_W6 = (
G_W4 = (
np.random.normal(
size=(hidden_input5, hidden_input6), scale=(1.0 / np.sqrt(hidden_input5 / 2.0))
size=(hidden_input3, hidden_input4),
scale=(1.0 / np.sqrt(hidden_input3 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b6 = np.zeros(hidden_input6)
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b4 = np.zeros(hidden_input4)
G_W7 = (
G_W5 = (
np.random.normal(
size=(hidden_input4, hidden_input5),
scale=(1.0 / np.sqrt(hidden_input4 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b5 = np.zeros(hidden_input5)
G_W6 = (
np.random.normal(
size=(hidden_input5, hidden_input6),
scale=(1.0 / np.sqrt(hidden_input5 / 2.0)),
)
* 0.002
)
# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.))) *0.002
G_b6 = np.zeros(hidden_input6)
G_W7 = (
np.random.normal(
size=(hidden_input6, 784), scale=(1.0 / np.sqrt(hidden_input6 / 2.0))
)
* 0.002
)
# G_b2 = np.random.normal(size=(784),scale=(1. / np.sqrt(784 / 2.))) *0.002
G_b7 = np.zeros(784)
)
# G_b2 = np.random.normal(size=(784),scale=(1. / np.sqrt(784 / 2.))) *0.002
G_b7 = np.zeros(784)
# 3. For Adam Optimzier
v1, m1 = 0, 0
v2, m2 = 0, 0
v3, m3 = 0, 0
v4, m4 = 0, 0
# 3. For Adam Optimzier
v1, m1 = 0, 0
v2, m2 = 0, 0
v3, m3 = 0, 0
v4, m4 = 0, 0
v5, m5 = 0, 0
v6, m6 = 0, 0
v7, m7 = 0, 0
v8, m8 = 0, 0
v9, m9 = 0, 0
v10, m10 = 0, 0
v11, m11 = 0, 0
v12, m12 = 0, 0
v5, m5 = 0, 0
v6, m6 = 0, 0
v7, m7 = 0, 0
v8, m8 = 0, 0
v9, m9 = 0, 0
v10, m10 = 0, 0
v11, m11 = 0, 0
v12, m12 = 0, 0
v13, m13 = 0, 0
v14, m14 = 0, 0
v13, m13 = 0, 0
v14, m14 = 0, 0
v15, m15 = 0, 0
v16, m16 = 0, 0
v15, m15 = 0, 0
v16, m16 = 0, 0
v17, m17 = 0, 0
v18, m18 = 0, 0
v17, m17 = 0, 0
v18, m18 = 0, 0
beta_1, beta_2, eps = 0.9, 0.999, 0.00000001
beta_1, beta_2, eps = 0.9, 0.999, 0.00000001
print("--------- Started Training ----------")
for iter in range(num_epoch):
print("--------- Started Training ----------")
for iter in range(num_epoch):
random_int = np.random.randint(len(images) - 5)
current_image = np.expand_dims(images[random_int], axis=0)
@ -494,5 +502,5 @@ for iter in range(num_epoch):
),
bbox_inches="tight",
)
# for complete explanation visit https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021
# -- end code --
# for complete explanation visit https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021
# -- end code --

20
web_programming/crawl_google_results.py
View File

@ -5,16 +5,18 @@ from bs4 import BeautifulSoup
from fake_useragent import UserAgent
import requests
print("Googling.....")
url = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
res = requests.get(url, headers={"UserAgent": UserAgent().random})
# res.raise_for_status()
with open("project1a.html", "wb") as out_file: # only for knowing the class
if __name__ == "__main__":
print("Googling.....")
url = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
res = requests.get(url, headers={"UserAgent": UserAgent().random})
# res.raise_for_status()
with open("project1a.html", "wb") as out_file: # only for knowing the class
for data in res.iter_content(10000):
out_file.write(data)
soup = BeautifulSoup(res.text, "html.parser")
links = list(soup.select(".eZt8xd"))[:5]
soup = BeautifulSoup(res.text, "html.parser")
links = list(soup.select(".eZt8xd"))[:5]
print(len(links))
for link in links:
print(len(links))
for link in links:
webbrowser.open(f"http://google.com{link.get('href')}")