On Mon, Jul 13, 2015 at 11:12 AM, Thushan Ganegedara <[email protected]>
wrote:

> Hello CD,
>
> Yes, it seems to be working fine now. But why does it show the axes in
> meters? Is this a d3 specific thing?
>

I think *m* stands for *Milli* here.

>
> On Mon, Jul 13, 2015 at 3:17 PM, Thushan Ganegedara <[email protected]>
> wrote:
>
>> Hi all,
>>
>> Thank you very much for pointing out. I'll get the latest update and see.
>>
>> On Mon, Jul 13, 2015 at 3:03 PM, CD Athuraliya <[email protected]>
>> wrote:
>>
>>> Hi Thushan,
>>>
>>> That method has been updated. Please get the latest. You might have to
>>> define your own case depending on predicted values.
>>>
>>> CD Athuraliya
>>> Sent from my mobile device
>>> On Jul 13, 2015 10:24 AM, "Nirmal Fernando" <[email protected]> wrote:
>>>
>>>> Great work Thushan! On the UI issues, @CD could help you. AFAIK actual
>>>> keeps the pointer to the actual label and predicted is the probability and
>>>> predictedLabel is after rounding it using a threshold.
>>>>
>>>> On Mon, Jul 13, 2015 at 7:14 AM, Thushan Ganegedara <[email protected]>
>>>> wrote:
>>>>
>>>>> Hi all,
>>>>>
>>>>> I have integrated H-2-O deeplearning to WSO2-ml successfully.
>>>>> Following are the stats on 2 tests conducted (screenshots attached).
>>>>>
>>>>> Iris dataset - 93.62% Accuracy
>>>>> MNIST (Small) dataset - 94.94% Accuracy
>>>>>
>>>>> However, there were few unusual issues that I had to spend lot of time
>>>>> to identify.
>>>>>
>>>>> *FrameSplitter does not work for any value other than 0.5. Any value
>>>>> other than 0.5, the following error is returned*
>>>>> (Frame splitter is used to split trainingData to train and valid sets)
>>>>> barrier onExCompletion for
>>>>> hex.deeplearning.DeepLearning$DeepLearningDriver@25e994ae
>>>>> ​java.lang.RuntimeException: java.lang.RuntimeException:
>>>>> java.lang.NullPointerException
>>>>> at
>>>>> hex.deeplearning.DeepLearning$DeepLearningDriver.trainModel(DeepLearning.java:382)​
>>>>>
>>>>> *​DeepLearningModel.score(double[] vec) method doesn't work. *
>>>>> The predictions obtained with ​score(Frame f) and score(double[] v) is
>>>>> shown below.
>>>>>
>>>>> *Actual, score(Frame f), score(double[] v)*
>>>>> ​0.0, 0.0, 1.0
>>>>> 1.0, 1.0, 2.0
>>>>> 2.0, 2.0, 2.0
>>>>> 2.0, 1.0, 2.0
>>>>> 1.0, 1.0, 2.0
>>>>>
>>>>> As you can see, score(double[] v) is quite poor.
>>>>>
>>>>> After fixing above issues, everything seems to be working fine at the
>>>>> moment.
>>>>>
>>>>> However, the I've a concern regarding the following method in
>>>>> view-model.jag -> function
>>>>> drawPredictedVsActualChart(testResultDataPointsSample)
>>>>>
>>>>> var actual = testResultDataPointsSample[i].predictedVsActual.actual;
>>>>>         var predicted =
>>>>> testResultDataPointsSample[i].predictedVsActual.predicted;
>>>>>         var labeledPredicted = labelPredicted(predicted, 0.5);
>>>>>
>>>>>         if(actual == labeledPredicted) {
>>>>>             predictedVsActualPoint[2] = 'Correct';
>>>>>         }
>>>>>         else {
>>>>>             predictedVsActualPoint[2] = 'Incorrect';
>>>>>         }
>>>>>
>>>>> why does it compare the *actual and labeledPredicted* where it should
>>>>> be comparing *actual and predicted*?
>>>>>
>>>>> Also, the *Actual vs Predicted graph for MNIST show the axis in
>>>>> "Meters" *(mnist.png) which doesn't make sense. I'm still looking
>>>>> into this.
>>>>>
>>>>> Thank you
>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Regards,
>>>>>
>>>>> Thushan Ganegedara
>>>>> School of IT
>>>>> University of Sydney, Australia
>>>>>
>>>>
>>>>
>>>>
>>>> --
>>>>
>>>> Thanks & regards,
>>>> Nirmal
>>>>
>>>> Associate Technical Lead - Data Technologies Team, WSO2 Inc.
>>>> Mobile: +94715779733
>>>> Blog: http://nirmalfdo.blogspot.com/
>>>>
>>>>
>>>>
>>
>>
>> --
>> Regards,
>>
>> Thushan Ganegedara
>> School of IT
>> University of Sydney, Australia
>>
>
>
>
> --
> Regards,
>
> Thushan Ganegedara
> School of IT
> University of Sydney, Australia
>



-- 
*CD Athuraliya*
Software Engineer
WSO2, Inc.
lean . enterprise . middleware
Mobile: +94 716288847 <94716288847>
LinkedIn <http://lk.linkedin.com/in/cdathuraliya> | Twitter
<https://twitter.com/cdathuraliya> | Blog <http://cdathuraliya.tumblr.com/>
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