A simplified (and more elegant) version: carefree-ml
A Python ML package mainly for educational use.
Implemented with Numpy
Machine learning algorithms implemented by pure numpy
Home Page: https://mlblog.carefree0910.me
License: MIT License
A simplified (and more elegant) version: carefree-ml
A Python ML package mainly for educational use.
Implemented with Numpy
可以在readme里把专栏链接挂上啊~~
论文想引用您的代码,您的论文出处可以分享下嘛?
当我使用mushroom这个数据集的时候,出现了以下错误,但是使用balloon1.0、balloon1.5数据的时候一切正常,应该怎么做呢?
Traceback (most recent call last): File "E:\Python_learning\MachineLearning\b_NaiveBayes\MultinomialNB.py", line 70, in <module> nb.fit( _x, _y ) File "E:\Python_learning\MachineLearning\b_NaiveBayes\Basic.py", line 39, in fit self.feed_data(x, y, sample_weight) File "E:\Python_learning\MachineLearning\b_NaiveBayes\MultinomialNB.py", line 17, in feed_data cat_counter = np.bincount(y) TypeError: Cannot cast array data from dtype('float64') to dtype('int64') according to the rule 'safe' [Finished in 0.2s]
您好,
读过您写的书,收获很大,谢谢。
有一个疑问,如题。Time.timeit利用wrapt.dectator来计算一个函数的执行时间。通常是没有问题的。但是如果是一个嵌套函数,外层函数的计时就是:内层函数执行时间+wrapt 自身执行的时间。如果内层函数被执行n次,那么外层函数的计时时间就多出了n*(wrapt 自身执行时间),这可能导致时间分析不准确。比如下面的例子, outer()的总时间被错误的多计时了(100000000*wrap时间)。不知道有没有办法在wrapt之间进行通讯,来扣除内部wrapt 自身的时间(不是inner函数的执行时间哈)
@timing.timeit()
def inner():
print("inner")
@timing.timeit()
def outter():
for i in range(100000000):
inner()
print("outer")
sum_sheet.clear()
actual_num.clear()
expected_benford.clear()
AttributeError: 'list' object has no attribute 'clear'
在win10环境中运行NN.py出现错误,matplotlib==2.0.2 python3.6.5 tensorflow_gpu1.8
安装PySide =1.1.1->1.2.4时出现module 'subprocess' has no attribute 'mswindows',请问能在win10环境中运行么,
请问TimingMeta类是什么意思啊?书上的版本没有
我用的是tensorflow2.x版本
这是出现在 _fully_connected_linear中的错误 在初始化权重的时候报错
Traceback (most recent call last):
File "D:\NNs_tensorflow\NNs.py", line 8, in
nn = Basic(model_param_settings={'n_epoch':100}).fit(x,y,snapshot_ratio=0)
File "D:\NNs_tensorflow\Base.py", line 149, in fit
self._build_model()
File "D:\NNs_tensorflow\BasicNN.py", line 44, in _build_model
net = self._fully_connected_linear(net, [current_dimension, n_unit], i)
File "D:\NNs_tensorflow\Base.py", line 101, in _fully_connected_linear
w = Toolbox.init_w(shape, "W{}".format(appendix))
File "D:\NNs_tensorflow\NNUtil.py", line 224, in init_w
return tf.Variable(tf.compat.v1.truncated_normal(shape,stddev=math.sqrt(2/sum(shape))),name=name)
TypeError: unsupported operand type(s) for +: 'int' and 'str'
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