chapter_6_2

k-평균

• 각각의 픽셀값 (3차원 -> 1차원 배열) 평균 구함

  • 픽셀의 평균값은 활용해서 사과, 바나나, 파인애플에 근사한 이미지를 추출한 것

• 어떻게 평균값을 구할 수 있을까?

  • k-평균 알고리즘 (k-Means) 알고리즘
  • 평균값 = Cluster Center = Centroid

데이터 불러오기

다음을 참고하라 : http://bit.ly/hg-06-2

!wget https://bit.ly/fruits_300_data -O fruits_300.npy

--2022-03-31 02:17:17--  https://bit.ly/fruits_300_data
Resolving bit.ly (bit.ly)... 67.199.248.11, 67.199.248.10
Connecting to bit.ly (bit.ly)|67.199.248.11|:443... connected.
HTTP request sent, awaiting response... 301 Moved Permanently
Location: https://github.com/rickiepark/hg-mldl/raw/master/fruits_300.npy [following]
--2022-03-31 02:17:17--  https://github.com/rickiepark/hg-mldl/raw/master/fruits_300.npy
Resolving github.com (github.com)... 192.30.255.112
Connecting to github.com (github.com)|192.30.255.112|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://raw.githubusercontent.com/rickiepark/hg-mldl/master/fruits_300.npy [following]
--2022-03-31 02:17:17--  https://raw.githubusercontent.com/rickiepark/hg-mldl/master/fruits_300.npy
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 3000128 (2.9M) [application/octet-stream]
Saving to: ‘fruits_300.npy’

fruits_300.npy      100%[===================>]   2.86M  --.-KB/s    in 0.05s   

2022-03-31 02:17:17 (56.9 MB/s) - ‘fruits_300.npy’ saved [3000128/3000128]

• 넘파이 파일을 불러옴

import numpy as np
import matplotlib.pyplot as plt

fruits = np.load('fruits_300.npy')
print(fruits.shape)
print(fruits.ndim)

(300, 100, 100)
3

• 3차원 (샘플개수, 너비, 높이)
• 2차원 (샘플개수, 너비 x 높이)

fruits_2d = fruits.reshape(-1, 100*100)
fruits_2d.shape

(300, 10000)

• k-평균 알고리즘 활용

from sklearn.cluster import KMeans
km = KMeans(n_clusters=3, random_state = 42)
km.fit(fruits_2d)

KMeans(n_clusters=3, random_state=42)

• 모형학습 후, labels

print(km.labels_)

[2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 0 2 0 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 0 0 2 2 2 2 2 2 2 2 0 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1]

• 직접 샘플의 개수 확인

print(np.unique(km.labels_, return_counts=True))

(array([0, 1, 2], dtype=int32), array([111,  98,  91]))

import matplotlib.pyplot as plt

def draw_fruits(arr, ratio=1):
    n = len(arr)    # n은 샘플 개수입니다
    # 한 줄에 10개씩 이미지를 그립니다. 샘플 개수를 10으로 나누어 전체 행 개수를 계산합니다. 
    rows = int(np.ceil(n/10))
    # 행이 1개 이면 열 개수는 샘플 개수입니다. 그렇지 않으면 10개입니다.
    cols = n if rows < 2 else 10
    fig, axs = plt.subplots(rows, cols, 
                            figsize=(cols*ratio, rows*ratio), squeeze=False)
    for i in range(rows):
        for j in range(cols):
            if i*10 + j < n:    # n 개까지만 그립니다.
                axs[i, j].imshow(arr[i*10 + j], cmap='gray_r')
            axs[i, j].axis('off')
    plt.show()

draw_fruits(fruits[km.labels_==0])

클러스터 중심

draw_fruits(km.cluster_centers_.reshape(-1, 100, 100), ratio=3)

print(km.transform(fruits_2d[100:101]))

[[3393.8136117  8837.37750892 5267.70439881]]

print(km.predict(fruits_2d[100:101]))

[0]

draw_fruits(fruits[100:101])

최적의 k-평균 찾기

inertia = []
for k in range(2, 7):
  km = KMeans(n_clusters = k, random_state=42)
  km.fit(fruits_2d)
  inertia.append(km.inertia_)
plt.plot(range(2, 7), inertia)
plt.show()

• 위 결과 최적의 k-평균은 3.0 정도 된다.

• chapter6. 비지도학습은 잘 안 쓰인다. 시각화 문법만 유의해서 살펴보자.

• Reference : 혼자 공부하는 머신러닝 + 딥러닝