You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
101 lines
3.3 KiB
101 lines
3.3 KiB
# datasets.py
|
|
# -----------
|
|
# Licensing Information: You are free to use or extend these projects for
|
|
# educational purposes provided that (1) you do not distribute or publish
|
|
# solutions, (2) you retain this notice, and (3) you provide clear
|
|
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
|
|
#
|
|
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
|
|
# The core projects and autograders were primarily created by John DeNero
|
|
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
|
|
# Student side autograding was added by Brad Miller, Nick Hay, and
|
|
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
|
|
|
|
|
|
import numpy as np
|
|
from tensorflow.examples.tutorials.mnist import input_data
|
|
from util import normal
|
|
|
|
_MNIST = None
|
|
def datasetA():
|
|
training = np.array([[-3,3],[-3,0],[-1,1],[1,5],[3,4],[1,-1],[3,2],[5,0]])
|
|
trainingLabels = np.ones((8,2))
|
|
trainingLabels[:4,0] = 0
|
|
trainingLabels[4:,1] = 0
|
|
|
|
validation = np.array([[3,0]])
|
|
validationLabels = np.array([[1,0]])
|
|
|
|
test = np.array([[-1,2]])
|
|
testLabels = np.array([[0,1]])
|
|
return [training,trainingLabels, validation, validationLabels, test, testLabels]
|
|
|
|
def datasetB():
|
|
X_1 = normal(shape=(100, 2), mean=1.5)*3
|
|
X_2 = normal(shape=(100, 2), mean=-1)*3
|
|
training = np.r_[X_1[:50], X_2[:50]]
|
|
trainingLabels = np.ones((100,2))
|
|
trainingLabels[50:,0] = 0
|
|
trainingLabels[:50,1] = 0
|
|
|
|
validation = np.r_[X_1[50:80], X_2[50:80]]
|
|
validationLabels = np.ones((60,2))
|
|
validationLabels[30:,0] = 0
|
|
validationLabels[:30,1] = 0
|
|
|
|
test = np.r_[X_1[80:], X_2[80:]]
|
|
testLabels = np.ones((40,2))
|
|
testLabels[20:, 0] = 0
|
|
testLabels[:20, 1] = 0
|
|
return [training, trainingLabels, validation, validationLabels, test, testLabels]
|
|
|
|
def tinyDataset():
|
|
training = np.array([[0,0,0], [1,0,0], [0,1,0], [0,0,1], [1,1,0], [0,1,1], [1,0,1], [1,1,1]])
|
|
trainingLabels = np.array([[0,1], [0,1], [0,1], [0,1], [0,1], [0,1], [1,0], [1,0]])
|
|
|
|
validation = np.array([[1,0,1]])
|
|
validationLabels = np.array([[0,1]])
|
|
|
|
test = np.array([[1,0,1]])
|
|
testLabels = np.array([[1,0]])
|
|
|
|
return [training, trainingLabels, validation, validationLabels, test, testLabels]
|
|
|
|
def mnistDataset():
|
|
global _MNIST
|
|
|
|
if _MNIST is None:
|
|
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
|
|
|
|
_MNIST = (mnist.train.images, mnist.train.labels,
|
|
mnist.validation.images, mnist.validation.labels,
|
|
mnist.test.images, mnist.test.labels)
|
|
|
|
return [data.copy() for data in _MNIST]
|
|
|
|
def largeMnistDataset():
|
|
all_data = mnistDataset()
|
|
training = all_data[0][:2000]
|
|
trainingLabels = all_data[1][:2000]
|
|
validation = all_data[2][:1000]
|
|
validationLabels = all_data[3][:1000]
|
|
test = all_data[4][:1000]
|
|
testLabels = all_data[5][:1000]
|
|
|
|
return [training, trainingLabels, validation, validationLabels, test, testLabels]
|
|
|
|
def medMnistDataset():
|
|
all_data = mnistDataset()
|
|
training = all_data[0][:1000]
|
|
trainingLabels = all_data[1][:1000]
|
|
validation = all_data[2][:200]
|
|
validationLabels = all_data[3][:200]
|
|
test = all_data[4][:200]
|
|
testLabels = all_data[5][:200]
|
|
|
|
return [training, trainingLabels, validation, validationLabels, test, testLabels]
|
|
|
|
def tinyMnistDataset():
|
|
all_data = mnistDataset()
|
|
return [data[:10] for data in all_data]
|