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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Feature Analysis
The standard features
Step1:  a. histogram
plot the histgram of one feature, to see what distribution the feature is.
Step2:  Different features have different ditributions.
Some are subject to Gussain distribution.
b. t-SNE
use TSNE to see the linear separability of the data.
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Python Code:
import numpy as np
import os
from sklearn.manifold import TSNE
from common import Data
lld=Data('lld')
lld.load_training_data()
print 'training feature shape: ', lld.feature.shape
print 'training label shape: ', lld.label.shape
#lld.load_test_data()
#print 'test feature shape: ',lld.feature_test.shape
#print 'test label shape: ',lld.label_test.shape
Explanation: Feature Analysis
The standard features: LLR (low level discriptors)
File path: \emotiondetection\features_labels_lld
load data
class Data. please see common.py
get the training data
End of explanation
import matplotlib.pyplot as plt
%matplotlib inline  
feature_table=[1,10,100,300]
for ind,fea in enumerate(feature_table):
    f= lld.feature[:,fea]
    
    plt.subplot(2,2,ind+1)
    plt.hist(f)
    #plt.title("Histogram of feature "+str(ind))
plt.axis('tight')
Explanation: a. histogram
plot the histgram of one feature, to see what distribution the feature is.
End of explanation
model=TSNE(n_components=2,random_state=0) # reduct the dimention to 2 for visualization
np.set_printoptions(suppress=True)
Y=model.fit_transform(lld.feature,lld.label) # the reducted data
plt.scatter(Y[:, 0], Y[:, 1],c=lld.label[:,0],cmap=plt.cm.Spectral)
plt.title('training data')
plt.axis('tight')
 
print Y.shape
Explanation: Different features have different ditributions.
Some are subject to Gussain distribution.
b. t-SNE
use TSNE to see the linear separability of the data.
End of explanation
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