Dataset classify

import tensorflow as tf
import matplotlib.pyplot as plt

import math
import numpy as np

from sklearn.datasets import load_iris

data=load_iris()

print(data)

print(data.keys())

features=data.data
feature_names=data.feature_names
target=data.target
target_names=data.target_names

for t in range(3):
    if t == 0:
        c='r'
        marker='>'
    elif t == 1:
        c='g'
        marker='o'
    elif t == 2:
        c='b'
        marker='x'
    plt.scatter(features[target == t, 0],   # sepal length
                features[target == t, 1],   # sepal width
                marker = marker,
                c = c)
                
plength=features[:,2]
labels=target_names[target]
is_setosa=(labels=='setosa')

max_setosa=plength[is_setosa].max()
min_non_setosa=plength[~is_setosa].min()
print('Maximun of setosa:{0}.'.format(max_setosa))
print('Minimun of other:{0}.'.format(min_non_setosa))

features=features[~is_setosa]
labels=labels[~is_setosa]
is_virginica=(labels == 'verginica')
best_acc=0.0

for feature in range(features.shape[1]):
    threshold=features[:,feature]
    for t in threshold:
        
        feature_i=features[:,feature]
        pred=(feature_i>t)
        acc=(pred == is_virginica).mean()
        rev_acc=(pred == ~is_virginica).mean()
        
        if rev_acc > acc:
            reverse = True
            acc =rev_acc
        else :
            reverse = False
        if acc > best_acc:
            best_acc =acc
            best_t = t
            best_reverse = reverse
print(best_t, best_reverse, best_acc)