Add RNN TS tutorial

PCA.py

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+import pandas as pd
+import numpy as np
+from sklearn.decomposition import PCA as skPCA
+import matplotlib.pyplot as plt
+from matplotlib import style
+from time import time
+style.use('ggplot')
+np.random.seed(12)
+
+class PCA(object):
+
+	def __init__(self, n_components):
+
+		self.n_components = n_components
+
+
+	def fit(self, X):
+		S = np.dot(X.T, X)
+		auto_val, auto_vect = np.linalg.eig(S)
+
+		sort_vect = np.argsort(auto_val)[::-1][:self.n_components]
+
+		self.auto_val = auto_val[sort_vect]
+		self.components_ = auto_vect[:, sort_vect]
+		self.m = X.mean(axis=0)
+
+
+	def transform(self, X):
+		print(np.round(self.auto_val, 3))
+		print(np.round(self.components_, 3))		
+		return np.dot((X - self.m), self.components_)
+
+
+
+def plot_pca(dados, tranf_dados, components):
+
+	pca1 = components[0]
+	pca2 = components[1]
+
+	for ii, jj in zip(tranf_dados, dados):
+		plt.scatter(pca1[0] * ii[0], pca1[1] * ii[0], color = 'red')
+		plt.scatter(pca2[0] * ii[1], pca2[1] * ii[1], color = 'c')
+		plt.scatter(jj[0], jj[1])
+	plt.xlim([-4, 4])
+	plt.ylim([-4, 4])	
+	plt.show()
+
+
+
+if __name__ == '__main__':
+
+	x1 = np.random.normal(1, 0.5, 50)
+	x2 = 2 * x1 + np.random.normal(0, 0.5, len(x1))
+	x3 = x1
+	dados = pd.DataFrame({'x1': x1, 'x2': x2, 'x3': x3}).values
+
+	pca = skPCA(n_components=2)
+	pca.fit(dados)
+	tranf_dados = pca.transform(dados)
+	plot_pca(dados, tranf_dados, pca.components_)
+
+	mypca = PCA(n_components=2)
+	mypca.fit(dados)	
+	tranf_dados = mypca.transform(dados)
+	plot_pca(dados, tranf_dados, pca.components_)