Visualise label distribution

viz.py

import numpy as np
import os
import dtdata as dt
from sklearn.neighbors import NearestNeighbors
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)

plt.rcParams['interactive'] == True

# fix random seed for reproducibility
np.random.seed(90210)
subset = -1

path =r'/home/suroot/Documents/train/daytrader/ema-crossover' # path to data
data = dt.loadData(path, subset)
(data, labels) = dt.centerAroundEntry(data)
print("min: " + str(labels.min()) )
print("max: " + str(labels.max()) )

sns.distplot(labels)  
plt.show()