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import requests | |
from bs4 import BeautifulSoup | |
import csv | |
file = open('Art-galleries-in-Malta.csv', 'w') | |
f = csv.writer(file) | |
f.writerow(['Name', 'Phone_no', 'Website', 'Address', 'State/Province']) | |
country = 'Malta' |
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def update_weights_MSE(m, b, X, Y, learning_rate): | |
m_deriv = 0 | |
b_deriv = 0 | |
N = len(X) | |
for i in range(N): | |
# Calculate partial derivatives | |
# -2x(y - (mx + b)) | |
m_deriv += -2*X[i] * (Y[i] - (m*X[i] + b)) | |
# -2(y - (mx + b)) |
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def update_weights_MAE(m, b, X, Y, learning_rate): | |
m_deriv = 0 | |
b_deriv = 0 | |
N = len(X) | |
for i in range(N): | |
# Calculate partial derivatives | |
# -x(y - (mx + b)) / |mx + b| | |
m_deriv += - X[i] * (Y[i] - (m*X[i] + b)) / abs(Y[i] - (m*X[i] + b)) | |
# -(y - (mx + b)) / |mx + b| |
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def update_weights_Hinge(m1, m2, b, X1, X2, Y, learning_rate): | |
m1_deriv = 0 | |
m2_deriv = 0 | |
b_deriv = 0 | |
N = len(X1) | |
for i in range(N): | |
# Calculate partial derivatives | |
if Y[i]*(m1*X1[i] + m2*X2[i] + b) <= 1: | |
m1_deriv += -X1[i] * Y[i] | |
m2_deriv += -X2[i] * Y[i] |
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from sklearn.datasets import make_blobs | |
# Create dataset with 3 random cluster centers and 1000 datapoints | |
x, y = make_blobs(n_samples = 1000, centers = 3, n_features=2, shuffle=True, random_state=31) |
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# importing requirements | |
from keras.layers import Dense | |
from keras.models import Sequential | |
from keras.optimizers import adam | |
# alpha = 0.001 as given in the lr parameter in adam() optimizer | |
# build the model | |
model_alpha1 = Sequential() | |
model_alpha1.add(Dense(50, input_dim=2, activation='relu')) |
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from sklearn.tree import DecisionTreeClassifier | |
clf_entropy = DecisionTreeClassifier(criterion = 'entropy', random_state = 33) | |
clf_entropy.fit(X, Y) |
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from sklearn.tree import DecisionTreeClassifier | |
clf_gini = DecisionTreeClassifier(criterion = 'gini', random_state = 33) | |
clf_gini.fit(X, Y) |
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from sklearn.datasets import make_regression | |
X, y = make_regression(n_samples = 20, n_features = 6, random_state = 2, noise = 0.5) |
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import statsmodels.api as sm | |
model = sm.OLS(y, X[:, 4]).fit() | |
model.summary() |
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