First , This paper mainly discusses and demonstrates the basic data model used in machine learning and its demonstration , The second is the in-depth learning discussion , then , discuss ANN and CNN How to predict results , for example , When rendering an unknown image ,CNN Will try to recognize it as belonging to one of the categories it has been trained to recognize .
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn import decomposition
from sklearn.preprocessing import scale
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('iris.csv', header=None, sep=',')
df.columns=['sepal_length', 'sepal_width', 'petal_length',
'petal_width', 'class']
df.dropna(how="all", inplace=True) # Drops empty line at EOF
# Show the first 5 records
print(df.head())
f, ax = plt.subplots(1, 4, figsize=(10,5))
vis1 = sns.distplot(df['sepal_length'],bins=10, ax= ax[0])
vis2 = sns.distplot(df['sepal_width'],bins=10, ax=ax[1])
vis3 = sns.distplot(df['petal_length'],bins=10, ax= ax[2])
vis4 = sns.distplot(df['petal_width'],bins=10, ax=ax[3])
plt.show()
# split data table into data X and class labels y
X = df.ix[:,0:4].values
y = df.ix[:,4].values
# Standardize the data
X_std = StandardScaler().fit_transform(X)
# Compute the covariance matrix
mean_vec = np.mean(X_std, axis=0)
cov_mat = (X_std -mean_vec).T.dot(X_std - mean_vec) /
(X_std.shape[0] - 1)
print('Covariance matrix \n%s' %cov_mat)
# Compute the Eigenvectors and Eigenvalues
cov_mat = np.cov(X_std.T)
eig_vals, eig_vecs = np.linalg.eig(cov_mat)
print('Eigenvectors \n%s' %eig_vecs)
print('Eigenvalues \n%s' %eig_vals)
eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in
range(len(eig_vals))]
eig_pairs.sort()
eig_pairs.reverse()
print('Eigenvalues in descending order:')
for i in eig_pairs:
print(i[0])
# Compute the Eigenvalue ratios
tot = sum(eig_vals)
var_exp = [(i / tot)*100 for i in sorted(eig_vals,
reverse=True)]
cum_var_exp = np.cumsum(var_exp)
print('Eigenvalue ratios:%s' %cum_var_exp)
#Create the W matrix
matrix_w = np.hstack((eig_pairs[0][1].reshape(4,1),
eig_pairs[1][1].reshape(4,1)))
print('Matrix W:\n', matrix_w)
# Transform the X_std dataset to the sub-space Y
Y = X_std.dot(matrix_w)
features = ['sepal_length', 'sepal_width', 'petal_length',
'petal_width']
# Create a scatter plot for PC1 vs PC2
x = df.loc[:,features].values
x = StandardScaler().fit_transform(x)
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(x)
principalDf = pd.DataFrame(data=principalComponents,
columns=['principal component 1','principal component 2'])
finalDf = pd.concat([principalDf, df[['class']]], axis=1)
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(1,1,1)
ax.set_xlabel('Principal Component 1', fontsize=15)
ax.set_ylabel('Principal Component 2', fontsize=15)
ax.set_title('2 Component PCA', fontsize=20)
targets = ['setosa', 'versicolor', 'virginica']
colors = ['r', 'g', 'b']
for target, color in zip(targets, colors):
indicesToKeep = finalDf['class'] == target
ax.scatter(finalDf.loc[indicesToKeep, 'principal component
1'], finalDf.loc[indicesToKeep, 'principal component 2'],
c=color, s=50)
ax.legend(targets)
ax.grid
plt.show()
