import sys
sys.path.append('C:/Users/akhram/Desktop/AIML/Machine Learning/Utils')
import classification_utils as cutils
from sklearn import model_selection
#2-d classification pattern
X, y = cutils.generate_linear_synthetic_data_classification(n_samples=500,
n_features=2,
n_classes=2,
weights=[0.4, 0.6],
class_sep=1)
cutils.plot_data_2d_classification(X, y)
X, y = cutils.generate_linear_synthetic_data_classification(
n_samples=500,
n_features=3,
n_classes=3,
weights=[0.4, 0.3, 0.3],
class_sep=1)
cutils.plot_data_3d_classification(X, y)
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, test_size=0.2, random_state=1)
cutils.plot_data_2d_classification(X_train, y_train)
import sys
sys.path.append("E:/utils")
import classification_utils as cutils
from sklearn import preprocessing
import numpy as np
X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000,
noise=0.1)
X, y = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=1000,
noise=0.1)
cutils.plot_data_2d_classification(X, y)
#guassian basis transformation
tmp = np.exp(-(X**2).sum(1))
X_3d = np.c_[X, tmp]
cutils.plot_data_3d_classification(
X_3d,
y,
new_window=True,
title="Linearly separable data in 3D with basis change")
#polynomial basis transformation
poly_features = preprocessing.PolynomialFeatures()
X_poly1 = poly_features.fit_transform(X)
poly_features = preprocessing.PolynomialFeatures(degree=3)
X_poly2 = poly_features.fit_transform(X)
import pandas as pd import os #2-d classification pattern X, y = cutils.generate_linear_synthetic_data_classification(n_samples=1000, n_features=2, n_classes=4, weights=[0.3,0.3,0.2,0.2]) cutils.plot_data_2d_classification(X, y) X, y = cutils.generate_nonlinear_synthetic_data_classification1(n_samples=1000, n_features=2, n_classes=2) cutils.plot_data_2d_classification(X, y) X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.05) cutils.plot_data_2d_classification(X, y) X, y = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=1000, noise=0.05) cutils.plot_data_2d_classification(X, y) #3-d classification pattern X, y = cutils.generate_linear_synthetic_data_classification(n_samples=1000, n_features=3, n_classes=5, weights=list(np.repeat(0.1,5))) cutils.plot_data_3d_classification(X, y) X, y = cutils.generate_nonlinear_synthetic_data_classification1(n_samples=1000, n_features=3, n_classes=2) cutils.plot_data_3d_classification(X, y) dir = 'E:/' titanic_train = pd.read_csv(os.path.join(dir, 'train.csv')) print(titanic_train.info()) features = ['Pclass', 'Fare', 'SibSp'] X_train = titanic_train[features] y_train = titanic_train['Survived'] cutils.plot_data_3d_classification(X_train, y_train)