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# import required functions and libraries | |
from sklearn.datasets import make_circles | |
from sklearn.neighbors import kneighbors_graph | |
from sklearn.cluster import SpectralClustering | |
import numpy as np | |
import matplotlib.pyplot as plt | |
# generate your data | |
X, labels = make_circles(n_samples=500, noise=0.1, factor=.2) |
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# get the image from "https://cdn.pixabay.com/photo/2017/03/27/16/50/beach-2179624_960_720.jpg" | |
import numpy as np | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
import cv2 | |
# read image in grayscale | |
img = cv2.imread('beach-2179624_960_720.jpg', 0) | |
# obtain svd |
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import numpy as np | |
from sklearn.utils.extmath import randomized_svd | |
A = np.array([[-1, 2, 0], [2, 0, -2], [0, -2, 1]]) | |
u, s, vt = randomized_svd(A, n_components = 2) | |
print("Left Singular Vectors:") | |
print(u) | |
print("Singular Values:") |
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import numpy as np | |
from sklearn.decomposition import TruncatedSVD | |
A = np.array([[-1, 2, 0], [2, 0, -2], [0, -2, 1]]) | |
print("Original Matrix:") | |
print(A) | |
svd = TruncatedSVD(n_components = 2) | |
A_transf = svd.fit_transform(A) |
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import numpy as np | |
from numpy.linalg import svd | |
# define your matrix as a 2D numpy array | |
A = np.array([[4, 0], [3, -5]]) | |
U, S, VT = svd(A) | |
print("Left Singular Vectors:") | |
print(U) |
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# create document term matrix for your data | |
# you can use TfidfVectorizer instead of CountVectorizer as well | |
from sklearn.feature_extraction.text import CountVectorizer | |
cvec = CountVectorizer() | |
docTermMat = cvec.fit_transform(data['text'].values) | |
# truncated SVD to preserve 20 topics | |
from sklearn.decomposition import TruncatedSVD | |
lsa = TruncatedSVD(n_components = 20, n_iter = 500) | |
lsa.fit(docTermMat) |
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from sklearn.decomposition import TruncatedSVD | |
// say you want to reduce to 2 features | |
svd = TruncatedSVD(n_features = 2) | |
//obtain the transformed data | |
data_transformed = svd.fit_transform(data) |
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# import required libraries | |
import numpy as np | |
import matplotlib.pyplot as plt | |
import cv2 | |
from skimage.color import rgb2gray | |
from scipy import ndimage | |
# read the image | |
img = cv2.imread('1.jpeg') |
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from sklearn.decomposition import PCA | |
// say you want to reduce to 2 features | |
pca = PCA(n_components = 2) | |
// obtain transformed data | |
data_transformed = pca.fit_transform(data) |
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