LDA
目次
LDA¶
import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib
from sklearn.datasets import make_blobs
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Input In [1], in <cell line: 2>()
1 import numpy as np
----> 2 import matplotlib.pyplot as plt
3 import japanize_matplotlib
4 from sklearn.datasets import make_blobs
ModuleNotFoundError: No module named 'matplotlib'
# 表示する文字サイズを調整
plt.rc("font", size=20)
plt.rc("legend", fontsize=16)
plt.rc("xtick", labelsize=14)
plt.rc("ytick", labelsize=14)
# youtube動画を表示
import IPython.display
# IPython.display.YouTubeVideo("XXX", width="500px")
実験用のデータ¶
X, y = make_blobs(
n_samples=600, n_features=3, random_state=11711, cluster_std=4, centers=3
)
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(projection="3d")
ax.scatter(X[:, 0], X[:, 1], X[:, 2], marker="o", c=y)
ax.set_xlabel("$x_1$")
ax.set_ylabel("$x_2$")
ax.set_zlabel("$x_3$")
Text(0.5, 0, '$x_3$')
LDAで二次元に次元削減する¶
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
lda = LDA(n_components=2).fit(X, y)
X_lda = lda.transform(X)
fig = plt.figure(figsize=(8, 8))
plt.scatter(X_lda[:, 0], X_lda[:, 1], c=y, alpha=0.5)
<matplotlib.collections.PathCollection at 0x7fbff7be5e20>
PCAとLDAの比較¶
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
fig = plt.figure(figsize=(8, 8))
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y, alpha=0.5)
<matplotlib.collections.PathCollection at 0x7fbff7fc7d00>
