]
images = []
for imgNames in imageNames:
images.append(getImageData(imgNames))
alphaIgnoredImages = []
for img in images:
imgWithoutAlpha = img[:,:,0:3]
# print("imgWithoutAlpha", imgWithoutAlpha.shape)
alphaIgnoredImages.append(imgWithoutAlpha)
# print("images", images, images[0].shape, images[1].shape, images[2].shape)
# print("alphaIgnoredImages", alphaIgnoredImages)
reshapedImages = []
for img in alphaIgnoredImages:
reshapedImg = img.reshape(1,-1)
reshapedImages.append(reshapedImg)
print("reshapedImages", reshapedImages, "dimension", reshapedImages[0].shape[1])
lshModel = LSH(noOfHashers=25, noOfHash=10, dimension=reshapedImages[0].shape[1])
for i in range(0, len(reshapedImages)):
lshModel.train(reshapedImages[i], { "name": imageNames[i] })
print(lshModel.isSimilar(reshapedImages[0], reshapedImages[1]))
print(lshModel.isSimilar(reshapedImages[0], reshapedImages[2]))
print(lshModel.isSimilar(reshapedImages[1], reshapedImages[2]))
print(lshModel.isSimilar(reshapedImages[2], reshapedImages[3]))
from sklearn.feature_extraction.text import CountVectorizer
from lsh.lsh import LSH
import numpy as np
texts = [
'Jack went to the market to buy some fruits',
'Jane went to the market to buy some fruits today',
'Robert and his team played hockey today'
]
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts).toarray().reshape(len(texts), 1, -1)
lshModel = LSH(noOfHashers=25, noOfHash=3, dimension=X.shape[2])
for i in range(0, X.shape[0]):
lshModel.train(X[i], {"name": texts[i]})
print(lshModel.isSimilar(X[0], X[1]))
print(lshModel.isSimilar(X[0], X[2]))
print(lshModel.isSimilar(X[1], X[2]))
