two-stage-tradaboost.r2's People
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regulusyy yangkaibigdata jinnywang igfalouji leiseraiesecqd lijinlong1991 joeyqiang949 raijin0704 diadochos mengc22 wuzunzun thn-buaa wanganbin1 george-jiexiongtwo-stage-tradaboost.r2's Issues
In the target domain, the dimension of the x_source is [100,6] and the dimension of the y_source is [100,1], which is an error
The dimension x_source in the example you gave is the same as the one y_source. If the x_source is one of multiple data pairs y_source, an error is reported.
ValueError: Input error: the specified sample size does not equal to the input size
I want to test high dimension data, but I met the valueError problem
My source domain input data has 11 features, X=[4898,11], output data Y=[4898,1], target domain input data also has 11 features. the train data is 79,
But I met a problem, the (error_vect[:-n_target]) * self.learning_rate),
ValueError: operands could not be broadcast together with shapes (4898,) (4898,4977) (4898,)
Do you know how to solve this problem? Thank you very much!!!
A possible bug
In line:
y_train = np.concatenate((y_source, y_source[train]))
I think it should be:
y_train = np.concatenate((y_source, y_target[train]))
I didn't read the paper but only in this case, the input shape makes sense. Thanks for your code!
Export Model
Hey Jay,
first at all, great work by performing that algorithm.
I want to use it with an ANN. I wrapped it with the keras wrapper function and it worked perfectly. Is it possible to get the trained model after performing the code, so that I'm able to use the keras save_model() function?
Greetings
Julian
Licensing
Hey,
good job on the code! It looks very clean and works well for me.
Could you please add a license to your code. I would like to use it for my Master Thesis.
Cheers
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