Ryota Bannai RyotaBannai

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Man's soil is still rich enough to direct his own life.

RyotaBannai / plot_matrix_n_evectors_w_svd.py

Created December 19, 2018 16:24

	ax = plt.gca()
	ax.set_xlabel('X'); ax.set_ylabel('Y')
	plt.scatter(X_raw[:, 0], X_raw[:, 1], c='#B8860B', alpha=0.5)
	plt.scatter(X_mean[0], X_mean[1], c='red', s=50)
	plt.axis('equal')

	for length, vector in zip(e_values, V):
	dir_ = -vector * 3 * np.sqrt(length) # Tweak the sign
	start = X_mean; end = start + dir_
	arrowprops = dict(arrowstyle='->',linewidth=2,

RyotaBannai / plot_equiprobability.py

Last active December 28, 2018 03:50

	from matplotlib.patches import Ellipse
	import matplotlib as mpl

	ax = plt.gca(); ax.set_xlabel('X'); ax.set_ylabel('Y')
	plt.scatter(X_raw[:, 0], X_raw[:, 1], c='#B8860B', alpha=0.5)
	plt.scatter(X_mean[0], X_mean[1], c='red', s=50)

	U_, s_, _ = LA.svd(np.cov(X, rowvar=False), full_matrices=False)
	angle = np.degrees(np.arctan2(U_[1,0], U_[0,0]))
	width, height = 2 * np.sqrt(s_)

RyotaBannai / scilearn_pca.py

Created December 19, 2018 16:46

	from sklearn.decomposition import PCA

	pca = PCA(n_components=2)
	pca.fit(X) # Apply PCA

RyotaBannai / plot_matrix_n_evectors_w_scilearn.py

Created December 19, 2018 16:47

	ax = plt.gca(); ax.set_xlabel('X'); ax.set_ylabel('Y')
	plt.scatter(X[:, 0], X[:, 1], alpha=0.3, color="#191970")
	plt.scatter(pca.mean_[0], pca.mean_[1], c='red', s=50)
	plt.axis('equal')

	for length, vector in zip(pca.explained_variance_, pca.components_):
	dir_ = vector * 3 * np.sqrt(length)
	start = pca.mean_; end = start + dir_
	arrowprops = dict(arrowstyle='->',linewidth=2,
	shrinkA=0, shrinkB=0, color='red', alpha=0.5)

RyotaBannai / load_digit_n_pcafit.py

Created December 19, 2018 16:49

	from sklearn.datasets import load_digits
	digits = load_digits()
	pca = PCA(n_components=10).fit(digits.data)

RyotaBannai / plt_cumsum_explained_variance_ratio.py

Created December 19, 2018 16:50

	plt.plot(np.cumsum(pca.explained_variance_ratio_),'o-', c='#663399', alpha=.5)
	plt.xlabel('Number of components')
	plt.ylabel('Cumulative explained variance')

RyotaBannai / plt_cumsum_explained_variance_ratio_2.py

Created December 19, 2018 16:51

	pca = PCA().fit(digits.data) # Blank inside the closed bracket
	plt.plot(np.cumsum(pca.explained_variance_ratio_),'o-', c='#663399', alpha=.5)
	plt.xlabel('Number of components')
	plt.ylabel('Cumulative explained variance')

RyotaBannai / heatmap_w_pca.py

Last active December 19, 2018 17:57

	import seaborn as sns
	sns.set()
	sns.despine(left=True)

	fig, axes = plt.subplots(2, 2, figsize=(15, 10), gridspec_kw=dict(hspace=.4, wspace=.3))
	title_settings={'fontsize':16}
	subtitles=['Inversed samples with {} components']*3

	# Plot Heatmap 1
	ax = sns.heatmap(digits.data, cbar=False, ax=axes[0, 0])

RyotaBannai / plot_digits.py

Created December 19, 2018 17:58

	_, axes = plt.subplots(2, 8, figsize=(8, 2),
	subplot_kw={'xticks':[], 'yticks':[]},
	gridspec_kw=dict(hspace=0.1, wspace=0.1))
	for i, ax in enumerate(axes.flat):
	ax.imshow(digits.data[i].reshape(8, 8),
	cmap='binary', interpolation='nearest', clim=(0, 16))

RyotaBannai / plot_rougher_digits.py

Created December 19, 2018 17:59

	pca = PCA(2)
	PC = pca.fit_transform(digits.data)
	inversed = pca.inverse_transform(PC)
	_, axes = plt.subplots(4, 10, figsize=(10, 4),
	subplot_kw={'xticks':[], 'yticks':[]},
	gridspec_kw=dict(hspace=0.1, wspace=0.1))
	for i, ax in enumerate(axes.flat):
	ax.imshow(inversed[i].reshape(8, 8),
	cmap='binary', interpolation='nearest', clim=(0, 16))