Yes it will be tricky. But that said, i think we should be able to simply
change the existing parsers to be more flexible , to accomodate at least
slightly more diverse inputs. For example, variable columns in CSV etc...
1I would pass the memory parameters in the args array directly. The hadoop
specific arguments must come before your custom arguments, so like this
String[] args = new String[]{"-Dmapreduce.map.memory.mb=12323","customOpt1"
ToolRunner.run(..args)
The tool runner takes care of putting the hadoop sp
Robin,
This is a great example of how the problem is a bit harder for Mahout.
Mnay of the large datasets of interest will not fit in memory. That means
that mixed memory and disk formats are important in this flow which also
makes API's more complicated.
On Sat, Feb 22, 2014 at 10:12 AM, Rob
Various existing frameworks like R or python/sklearn already have existing
interfaces so would be good to follow some existing patterns. I personally find
sklearn's transform/fit/predict pattern very easy to use and understand.
Sent from my iPhone
On 22 Feb 2014, at 09:15, Gokhan Capan wrote:
I'm personally positive on this.
Could you give an example code snippet that shows how the usage is going to be?
Sent from my iPhone
> On Feb 22, 2014, at 5:37, Jay Vyas wrote:
>
> Hi dead. Sure I will take a look.
>
>
>> On Fri, Feb 21, 2014 at 7:51 PM, Ted Dunning wrote:
>>
>> Great idea.
