本文整理汇总了Python中loader.Loader.load_dataset方法的典型用法代码示例。如果您正苦于以下问题：Python Loader.load_dataset方法的具体用法？Python Loader.load_dataset怎么用？Python Loader.load_dataset使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类loader.Loader的用法示例。
在下文中一共展示了Loader.load_dataset方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: knn
# 需要导入模块: from loader import Loader [as 别名]
# 或者: from loader.Loader import load_dataset [as 别名]
if args.kmeans:
if args.kmeansk:
self.choices[0] = ("kmeans", args.kmeansk)
else:
self.choices[0] = ("kmeans", [])
if args.knn:
if args.knnk:
self.choices[1] = ("knn", args.knnk)
else:
self.choices[1] = ("knn", [])
self.run_prediction()
sys.exit()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Choose the algorithms to test on handwritten digit recognition.")
parser.add_argument("-m", "--kmeans", help="Run kmeans", action = "store_true")
parser.add_argument("-n","--knn", help="Run knn, if no k-value specified, default is used", action = "store_true")
parser.add_argument("-nk", "--knnk", metavar='N', type=int, nargs='+',help= "Choose k-values for knn (type -nk # # #)")
parser.add_argument("-mk", "--kmeansk", metavar='N', type=int, choices = [10,15,20],help= "Choose k-values for kmeans (type -mk # # #) (choose from 10,15,20)")
args = parser.parse_args()
if not(args.kmeans or args.knn):
parser.print_help()
else:
load = Loader("testing")
images, labels = load.load_dataset()
PredictionRunner(args, images, labels)
示例2: Loader
# 需要导入模块: from loader import Loader [as 别名]
# 或者: from loader.Loader import load_dataset [as 别名]
import numpy as np
from loader import Loader
import matplotlib.pyplot as plt
load = Loader("testing")
# Load in the digits from the dataset, each in their own array
zeros, labels = load.load_dataset([0])
ones, labels = load.load_dataset([1])
twos, labels = load.load_dataset([2])
threes, labels = load.load_dataset([3])
fours, labels = load.load_dataset([4])
fives, labels = load.load_dataset([5])
sixes, labels = load.load_dataset([6])
sevens, labels = load.load_dataset([7])
eights, labels = load.load_dataset([8])
nines, labels = load.load_dataset([9])
# Find the minimum number of digits so we don't go outside
# the bounds of the array
lengths = [len(zeros),len(ones),len(twos),len(threes),\
len(fours),len(fives),len(sixes),len(sevens),len(eights)]
length = min(lengths)
# Set up checking for similarity level between the same digits
sim0 = []
sim1 = []
sim2 = []