Base de données

from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
 
# How to create a dataset for a clustering problem
X, y = make_blobs(
        n_samples = 1000,
        n_features = 2,
        centers = 5,
        cluster_std = 0.5,
        shuffle = True,
        random_state = 9)
 
plt.scatter(X[:,0],
            X[:,1],
            c = ["#006c4b"])
plt.figure(figsize=(16,10)).show()
 

from sklearn.datasets import make_classification
import matplotlib.pyplot as plt
 
# How to create a dataset for a classification problem
variables, target  = make_classification(
                    n_samples = 1000,
                    n_features = 12,
                    n_informative = 7,
                    n_redundant = 3,
                    n_repeated = 2,
                    n_classes = 4,
                    # Distribution of classes 20% Output1
                    # 20%> output 2, 30% output 3 and 4        
                    weights = [.2,.2, .3, .3],
                    random_state = 8)
 
plt.scatter(variables[:,0],
            variables[:,1],
            c = ["#006c4b"])
plt.figure(figsize=(16,10)).show()
 

from sklearn.datasets import make_regression
import matplotlib.pyplot as plt
 
# How to create a dataset for a regression problem
variables, target = make_regression(n_samples = 1000,
                                    n_features =  10,
                                    n_informative = 8,
                                    n_targets = 1,
                                    noise = .5,
                                    random_state = 8
                                    )
 
plt.scatter(variables[:,0],
            variables[:,1],
            c = ["#006c4b"])
plt.figure(figsize=(16,10)).show()
 

#importing the libraries
import os 
import cv2
import numpy as np
from matplotlib import pyplot as plt
 
#setting the path to the directory containing the pics
path = 'Dataset-images'
 
#appending the pics to the training data list
training_data = []
for img in os.listdir(path):
    pic = cv2.imread(os.path.join(path,img))
    pic = cv2.cvtColor(pic,cv2.COLOR_BGR2RGB)
    pic = cv2.resize(pic,(256,256))
    training_data.append([pic])
 
#converting the list to numpy array and saving it to a file using #numpy.save
np.save(os.path.join(path,'features'),np.array(training_data))
 
#loading the saved file once again
saved = np.load(os.path.join(path,'features.npy'))
 
for i in range(4):
 
    #subplot
 
    plt.subplot(5, 5, i+1)
 
    # plotting pixel data
 
    plt.imshow(np.array(training_data[i]).reshape(256,256,3))
 
# show the figure
 
plt.show()
 
plt.imshow(saved[0].reshape(256,256,3))
plt.imshow(np.array(training_data[0]).reshape(256,256,3))
plt.show()
plt.imshow(np.array(training_data[1]).reshape(256,256,3))
plt.show()
plt.imshow(np.array(training_data[2]).reshape(256,256,3))
plt.show()
plt.imshow(np.array(training_data[3]).reshape(256,256,3))
plt.show()
 

# -*- coding: utf-8 -*-
"""
Created on Sat Jun 18 12:35:28 2022
 
@author: https://www.tensorflow.org/datasets
"""
 
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
 
# Construct a tf.data.Dataset
ds = tfds.load('mnist', split='train', shuffle_files=True)
 
# Build your input pipeline
ds = ds.shuffle(1024).batch(32).prefetch(tf.data.experimental.AUTOTUNE)
for example in ds.take(1):
  image, label = example["image"], example["label"]