CS_ALearn/Book4_Power-of-Matrix
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Visualize-ML
2022-09-02 06:06:41 -04:00
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parent f68f6e700b
commit 9822d0a7e5
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Book4_Ch02_Python_Codes/Bk4_Ch2_05.py
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@@ -8,30 +8,10 @@
# Bk4_Ch2_05.py
from scipy.spatial import distance
from sklearn import datasets
import numpy as np
# import the iris data
iris = datasets.load_iris()
# define a column vector
a = np.array([[2], [2]])
# Only use the first two features: sepal length, sepal width
X = iris.data[:, :]
# Extract 4 data points
x1_data = X[0,:]
x2_data = X[1,:]
x51_data = X[50,:]
x101_data = X[100,:]
# calculate cosine distance
x1_x2_cos_dist = distance.cosine(x1_data,x2_data)
x1_norm = np.linalg.norm(x1_data)
x2_norm = np.linalg.norm(x2_data)
x1_dot_x2 = x1_data.T@x2_data
x1_x2_cos = x1_dot_x2/x1_norm/x2_norm
x1_x51_cos_dist = distance.cosine(x1_data,x51_data)
x1_x101_cos_dist = distance.cosine(x1_data,x101_data)
b = 2*a
c = -1.5*a