def datly():
# import
features_train, labels_train, features_test, labels_test = makeTerrainData(
)
def plotly(grade_fast, bumpy_fast, grade_slow, bumpy_slow):
plt.xlim(0.0, 1.0)
plt.ylim(0.0, 1.0)
plt.scatter(bumpy_fast, grade_fast, color="b", label="fast")
plt.scatter(grade_slow, bumpy_slow, color="r", label="slow")
plt.legend()
plt.xlabel("bumpiness")
plt.ylabel("grade")
plt.show()
def composite():
# обучающие данные (features_train, labels_train)
# имеют как "быстрый", так и " медленный" точки смешиваются
# вместе-разделите их, чтобы мы могли дать им разные цвета
# в диаграмме рассеяния и определить их визуально
grade_fast = [
features_train[ii][0] for ii in range(0, len(features_train))
if labels_train[ii] == 0
]
bumpy_fast = [
features_train[ii][1] for ii in range(0, len(features_train))
if labels_train[ii] == 0
]
grade_slow = [
features_train[ii][0] for ii in range(0, len(features_train))
if labels_train[ii] == 1
]
bumpy_slow = [
features_train[ii][1] for ii in range(0, len(features_train))
if labels_train[ii] == 1
]
plt = plotly(grade_fast, bumpy_fast, grade_slow, bumpy_slow)
return features_train, labels_train, features_test, labels_test, plt
from os import path import sys sys.path.append(path.dirname(path.dirname(path.abspath(__file__)))) from choose_your_own.class_vis import prettyPicture, output_image from choose_your_own.prep_terrain_data import makeTerrainData import matplotlib.pyplot as plt import numpy as np import pylab as pl features_train, labels_train, features_test, labels_test = makeTerrainData() ################################################################################# ########################## DECISION TREE ################################# # create/fit the classifier from sklearn import tree clf2 = tree.DecisionTreeClassifier(min_samples_split=2) clf2.fit(features_train, labels_train) clf50 = tree.DecisionTreeClassifier(min_samples_split=50) clf50.fit(features_train, labels_train) # preds pred2 = clf2.predict(features_test) pred50 = clf50.predict(features_test) #prettyPicture(clf, features_test, labels_test) #plt.show()