Mehdi-Amine

# 1- Generating a dataset.
from sklearn.datasets import make_moons
 
# X are the generated instances, an array of shape (500,2).
# y are the labels of X, with values of either 0 or 1.
X, y = make_moons(n_samples=500, noise=0.3, random_state=42)

# 2- Visualizing the dataset.
from matplotlib import pyplot as plt

Mehdi-Amine

# Creating three models with three different algorithms
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC

tree_clf = DecisionTreeClassifier(random_state=42)
log_clf = LogisticRegression(solver="lbfgs", random_state=42)
svm_clf = SVC(gamma="scale", random_state=42)

# Training, predicting, then evaluating the predictions

Mehdi-Amine

# Combining the three models into an ensemble
from sklearn.ensemble import VotingClassifier

# The ensemble is a voting classifier that aggregates our three models
voting_clf = VotingClassifier(estimators=[('svm', svm_clf), ('tree', tree_clf), ('log', log_clf)], 
                             voting='hard')

voting_clf.fit(X_train, y_train) # training
y_pred_voting = voting_clf.predict(X_test) # predicting
accuracy_score(y_test, y_pred_voting) # evaluating

Mehdi-Amine

# Out of the three models, 
# only SVC requires some tweaking to output its confidence
# this is done by setting probability=True:
svm_clf_tweaked = SVC(gamma='scale', probability=True, random_state=42)

soft_voting_clf = VotingClassifier(estimators=[('svm', svm_clf_tweaked), ('tree', tree_clf), ('log', log_clf)], 
                             voting='soft')

soft_voting_clf.fit(X_train, y_train) # training
y_pred_voting = soft_voting_clf.predict(X_test) # predicting

Mehdi-Amine

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

Mehdi-Amine

# Bagging creates several models that rely on the same algorithm.
# The training of each model uses a different subset of data sampled randomly from the training set.
# By default Bagging uses soft voting when its base estimator can provide its measure of confidence,
# Hence the SVC model is set to have probability=True
from sklearn.svm import SVC
from sklearn.ensemble import BaggingClassifier

bagging_clf = BaggingClassifier(SVC(gamma='scale', probability=True, random_state=42),
                                bootstrap=True, # set to False to use Pasting instead of Bagging
                                n_estimators=100, # number of SVC models to create

Mehdi-Amine

import numpy as np 
bias = np.random.randn(1, 1)
weights = np.random.randn(input_length, 1) # for some integer input_length

Mehdi-Amine

def sigmoid(z):
  return 1.0 / (1.0 + np.exp(-z))
z = numpy.dot(weights, instance) + bias.squeeze()
a = sigmoid(z)

Mehdi-Amine

def cost_derivative(activation, y):
    return (activation - y)

Mehdi-Amine

import numpy as np 

np.random.seed(42)
dx = np.random.randint(low=-10, high=11, size=(100,2)).astype(float)
dy = (dx[:,0] + dx[:,1]*0.5)
dx_train, dx_test, dy_train, dy_test = dx[:80], dx[80:], dy[:80], dy[80:]
training_data = list(zip(dx_train, dy_train))
testing_data = list(zip(dx_test, dy_test))

print(f"The first 5 rows of the dataset:\n{np.concatenate((dx, dy.reshape(-1,1)), axis=1)[:5]}")