Hi Jing, > On Oct 20, 2015, at 9:13 PM, fuyajing <[email protected]> wrote: > > If I set 'inferenceMode' of sp and tp to be 1, will claclassifier get > right output normally?
Yes. > In what kind of situation, should we set 'inferenceMode' to be 0? When you want the network to learn from the input without needing to extract classifications from it. Disabling inference will speed up the network. (Maybe use it during a purely training phase.) > > If I have a multi input [4,6,9,12], 4 is the predicted fields. So the > actualInput is 4. And assume the output encoder is 0001 0010 0100 1000, the > tp's bottomUpOutput is 0001010000001, it means the 3rd,5th,12th cells are > active. The 3rd cell corresponds to 2 list which are one-to-one > corresponding. (Just as Subutai's example in video[3]). The detailed meaning > of the 2 list is what I can't understand. I think it has relationship with > results I mentioned in previous email. Sorry, I still don't understand what you're trying to do. It might help to see the relevant code you have written that uses this. > My meaning is, can we judge which field contributes the predicted field > directly by the permanence or synapse connection? Or another kind of > explanation: when the input data is encoded into SDR, we can't distinguish > which bit represents each input field? We can only judge from the output of > classifier. For example, Encode field 1, 2 together and get claclassifier > result. Then encode field 1,3 together and get claclassifier result. Then > field 1,4 1,5 and so on. At last compare each result and judge witch > filed(2,3,4,5) contribute field 1. We don't usually do something like this. What are you trying to do that needs this functionality? Perhaps there's a better way to achieve what you want. - Chetan > > ------------------ 原始邮件 ------------------ > 发件人: "Chetan Surpur";<[email protected]>; > 发送时间: 2015年10月21日(星期三) 凌晨1:44 > 收件人: "fuyajing"<[email protected]>; > 主题: Re: A tp program question > > Hi Jing, > >> >> 1. I know when the parameter "learningmode" is set to 1, the connections >> between each column and cell will keep updating. But how to set parameter >> "inferencemode" ? If I have a network sensor-->sp-->tp-->claclassifier, it >> is obvious that we should set claclassifier's "inferencemode" to be 1 >> because we want to get the output result of claclassifier. My question is >> how to set sp's and tp's "inferencemode" (both sp's and tp 's learningmode >> are 1 for learning continually)? > > You do not need to worry about setting `inferenceMode` to 1, it is 1 by > default. > >> 2. Does likelihood has the relationship with prediction? The "result"[4] is >> a dict, which has two pairs of key-value. One is 1:list1, the other is >> actualValues:list2. List1 and list2 are one-to-one correspondence. We get >> the actual values in list2 which has the biggest likelihood in list1. And >> the sum of list1 equals 1. But I didn't understand how the two lists are >> calculated. The bucketIndex gets from >> scalarEncoder.getBucketIndcex(actualInput) is a number, and list1's index is >> a bucket range. What's the corresponding relationship between bucketIndex >> and list1? How to define list1's length and bucket range? > > I'm sorry, I don't really understand your question. Perhaps you can rephrase > it? Maybe it would help to see example values for the `results` dict you are > talking about. > >> 3. In a multiEncoder network, if I want to predict field1, can I know which >> fields contribute to field1? Not by swarming. > > I'm not sure what you mean. Swarming is a way to automatically discover which > fields help the most with the prediction of field1. You can also try > different combinations of fields by hand. > >> 4. Last time you suggest me to set a parameter to get connection >> relationship. I set the 'temporalImp' in file[3] to 'tm_py' but get the >> error as follows. > > It looks like you don't have the latest version of nupic. Please update your > repository and try it again. > > - Chetan > >> >> ------------------ 原始邮件 ------------------ >> 发件人: "Chetan Surpur";<[email protected] <mailto:[email protected]>>; >> 发送时间: 2015年10月17日(星期六) 凌晨2:52 >> 收件人: "fuyajing"<[email protected] <mailto:[email protected]>>; >> 主题: Re: A tp program question >> >> Jing, >> >> > Both spRegion and tpRegion have an attribute "bottomUpOut", which are >> > arrays(0 and 1). How to decode the 01 array into a predicted value? >> >> This is what the CLA classifier is used for. It takes these binary output >> arrays, and decodes the input value to the encoder that is the most likely >> prediction. >> >> > The multiEncoder is not SDR though there is little 1 and much 0.(Or >> > multiEncoder is sparse, not distributed? Which explanation is right?) >> >> It is distributed, but not necessarily sparse. It produces sparse >> representations only if the inputs to the MultiEncoder are all sparse. >> Either way, the output of the SPRegion is sparse and distributed. >> >> > So to top level (for example, classifier), we get a map from final >> > active columns index (we call it 'finalColumns') to sourceData. >> >> Correct, this is what the CLA classifier does in essence. You can just use >> it on top of the TPRegion to do this classification. >> >> > What's more, I can't understand the code segment in >> > nupic/examples/network/hierachy-network-demo.py.(line 267-272) >> >> The `results` dictionary is produced by the CLA Classifier. If you would >> like to understand the CLA Classifier better, please see Subutai's recent >> talk on how it works [1]. >> >> [1] https://www.youtube.com/watch?v=QZBtaP_gcn0 >> <https://www.youtube.com/watch?v=QZBtaP_gcn0> >> >> Hope this helps, >> Chetan >> >> >>> On Oct 15, 2015, at 12:34 AM, fuyajing <[email protected] >>> <mailto:[email protected]>> wrote: >>> >>> And another question: Both spRegion and tpRegion have an attribute >>> "bottomUpOut", which are arrays(0 and 1). How to decode the 01 array into a >>> predicted value? >>> For easier understanding, I want to express my understanding. If I have a >>> wrong explanation, please tell me. Thank you in advance. :) >>> For example, each attribute's value can be encoded into a 01 array, by >>> setting params w, n(or radius, or resolution) , max, min and so on. >>> A-->1111 0000 0000 0000 >>> B-->0000 1111 0000 0000 >>> C--> 0000 0000 1111 0000 >>> Monday-->111000000000 >>> Tuesday -->011100000000 >>> Thursday-->001110000000 >>> ... >>> Then if there are two attribute need to be encoded, we can just concatenate >>> them simply, we call it multiEncoder. >>> AMonday-->1111 0000 0000 0000 111000000000 >>> The multiEncoder is not SDR though there is little 1 and much 0.(Or >>> multiEncoder is sparse, not distributed? Which explanation is right?) It >>> just the input of the first spRegion. The out put of spRegion and tpRegion >>> is SDR. ( We can get it from "bottomUpOut"). From the SDR, we can't >>> distinguish which columns represent for which input bits, right? So to top >>> level (for example, classifier), we get a map from final active columns >>> index (we call it 'finalColumns') to sourceData. (If we want to predict A, >>> it's a map from finalColumns to A; if we want to predict Monday, it's a map >>> from finalColumns to Monday.) I think maybe it's the meaning of decode for >>> HTM. The column doesn't have a specific correspondence. Moreover, how to >>> initialize the connection relationship between the input 01 array and first >>> spRegion's columns? >>> >>> What's more, I can't understand the code segment in >>> nupic/examples/network/hierachy-network-demo.py.(line267-272) >>> >>> results = >>> classifier.getSelf().customCompute(recordNum=recordNumber,patternNZ=tpOutput,classification=classDict) >>> mostLikelyResult = sorted(zip(results[1], results["actualValues"]))[-1] >>> >>> How does each element of "results" come out? And I found the lengths of >>> result[1] and results["actualValues"] are variable lengths. >>> >>> Looking forward to hear from you at earliest. >>> >>> Jing >>> >>> >>> >>> >>> ------------------ 原始邮件 ------------------ >>> 发件人: "付亚静";<[email protected] <mailto:[email protected]>>; >>> 发送时间: 2015年10月15日(星期四) 中午1:21 >>> 收件人: "fuyajing"<[email protected] <mailto:[email protected]>>; >>> 主题: A tp program question >>> >>> Hello >>> I'm using network API to make predictions. The tp_params has an attribute >>> "cellsPerColumn". My question is: How to get the synapses connection >>> between columns or cells? Are there any method or code? >>> Thank you in advance. >>> >>> Jing >> >
