FORMAT: 1A Host: https://leancloud.cn/1.1/functions/

Senz Core Algo Service

senz core algo service provide a restful API for :

• training an event model

• and predicting the event type of a behavior sequence

Train a event model with specific observations [/trainWithSpecificObs/]

You can specify which model need training by tag. And you should give observations as a training sample. The format of training sample is following.

• Hint If you need store the result of training, you need add a key, named "description". The value of "description" is a memo of this train. And when you get the same tag model next time, you will get a whole new model params.

[
    [{"motion": "walking", "location": "residence", "sound": "tree"}, ...],
    [{"motion": "walking", "location": "residence", "sound": "tree"}, ...],
    ...
]

Train with specific observations [POST]

• Request (application/json)

  • Header

      X-AVOSCloud-Application-Id  : dkc5xdbwprsrh9809kqwopja5ckfbsrpd7dz9a30yugm9tut,
      X-AVOSCloud-Application-Key : 3sy9w8uwlr35xl54lja3rawyf8xjrhofxtvcwzng3blg7q31
    

  • Body

      {
      "algoType": "GMMHMM",
      "tag": "random_generated_base_model",
      "eventLabel": "dining.chineseRestaurant",
      "obs": [
          [
              {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
              {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
              {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
              {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
              {"motion": "walking", "sound": "others", "location": "chinese_restaurant"},
              {"motion": "walking", "sound": "tableware", "location": "chinese_restaurant"}
          ],
          [
              {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
              {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
              {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
              {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
              {"motion": "walking", "sound": "others", "location": "chinese_restaurant"},
              {"motion": "walking", "sound": "tableware", "location": "chinese_restaurant"}
          ]
      ],
      "description": "test for api." (optional)
      }
    

• Response 201 (application/json)

    {
    "result":{
        "code":0,
        "model":{
            "nMix":4,
            "nComponent":4,
            "hmmParams":{
                "transMat":[
                    [0.9937524708135964,2.6823758143694116e-18,0.00624752918640371,2.6823758143694116e-18],[0.25,0.25,0.25,0.25],[3.5716227445160065e-17,3.5716227445160065e-17,0.9999999999999999,3.5716227445160065e-17],[0.25,0.25,0.25,0.25]
                ],
                "startProb":[1,2.2205460492503137e-17,2.2205460492503137e-17,2.2205460492503137e-17]
            },
            "gmmParams":{
                "nMix":4,
                "covarianceType":"full",
                "params":[
                    {"covars":[[[0.20272925448170726,-0.21372410825705002,0.6640076953697146],[-0.21372410825704996,44.41361612877133,-3.8315142667592568],[0.6640076953697147,-3.831514266759256,29.058072351569287]],[[0.19961779798118065,-0.20567068761490478,0.6180902066504756],[-0.2056706876149048,24.5623976379672,-1.8862253825381476],[0.6180902066504755,-1.886225382538147,28.57872805315829]],[[0.21297430879173704,-0.12933325429515627,0.6502230892315992],[-0.12933325429515624,25.319590716568648,-1.135216161881697],[0.6502230892315992,-1.135216161881697,28.002589075295308]],[[0.20772196929306083,-0.16035047001514102,0.6106797655515614],[-0.16035047001514105,26.54555785898669,-1.643554371696736],[0.6106797655515614,-1.643554371696736,28.157741030750508]]],"weights":[0.24391800630360175,0.2623161644960391,0.2516074177870681,0.242158411413291],"means":[[0.3093785189118171,98.50767974689373,12.04201267494459],[0.2957223232822461,100.29260369087528,11.368513289910661],[0.32869103266615624,100.24202740519003,12.299965535058508],[0.31513728410310543,100.08656506960568,12.162225242103984]]},{"covars":[[[0.20272925448170726,-0.21372410825705002,0.6640076953697146],[-0.21372410825704996,44.41361612877133,-3.8315142667592568],[0.6640076953697147,-3.831514266759256,29.058072351569287]],[[0.19961779798118065,-0.20567068761490478,0.6180902066504756],[-0.2056706876149048,24.5623976379672,-1.8862253825381476],[0.6180902066504755,-1.886225382538147,28.57872805315829]],[[0.21297430879173704,-0.12933325429515627,0.6502230892315992],[-0.12933325429515624,25.319590716568648,-1.135216161881697],[0.6502230892315992,-1.135216161881697,28.002589075295308]],[[0.20772196929306083,-0.16035047001514102,0.6106797655515614],[-0.16035047001514105,26.54555785898669,-1.643554371696736],[0.6106797655515614,-1.643554371696736,28.157741030750508]]],"weights":[0.24391800630360175,0.2623161644960391,0.2516074177870681,0.242158411413291],"means":[[0.3093785189118171,98.50767974689373,12.04201267494459],[0.2957223232822461,100.29260369087528,11.368513289910661],[0.32869103266615624,100.24202740519003,12.299965535058508],[0.31513728410310543,100.08656506960568,12.162225242103984]]},{"covars":[[[0.20272925448170726,-0.21372410825705002,0.6640076953697146],[-0.21372410825704996,44.41361612877133,-3.8315142667592568],[0.6640076953697147,-3.831514266759256,29.058072351569287]],[[0.19961779798118065,-0.20567068761490478,0.6180902066504756],[-0.2056706876149048,24.5623976379672,-1.8862253825381476],[0.6180902066504755,-1.886225382538147,28.57872805315829]],[[0.21297430879173704,-0.12933325429515627,0.6502230892315992],[-0.12933325429515624,25.319590716568648,-1.135216161881697],[0.6502230892315992,-1.135216161881697,28.002589075295308]],[[0.20772196929306083,-0.16035047001514102,0.6106797655515614],[-0.16035047001514105,26.54555785898669,-1.643554371696736],[0.6106797655515614,-1.643554371696736,28.157741030750508]]],"weights":[0.24391800630360175,0.2623161644960391,0.2516074177870681,0.242158411413291],"means":[[0.3093785189118171,98.50767974689373,12.04201267494459],[0.2957223232822461,100.29260369087528,11.368513289910661],[0.32869103266615624,100.24202740519003,12.299965535058508],[0.31513728410310543,100.08656506960568,12.162225242103984]]},{"covars":[[[0.20272925448170726,-0.21372410825705002,0.6640076953697146],[-0.21372410825704996,44.41361612877133,-3.8315142667592568],[0.6640076953697147,-3.831514266759256,29.058072351569287]],[[0.19961779798118065,-0.20567068761490478,0.6180902066504756],[-0.2056706876149048,24.5623976379672,-1.8862253825381476],[0.6180902066504755,-1.886225382538147,28.57872805315829]],[[0.21297430879173704,-0.12933325429515627,0.6502230892315992],[-0.12933325429515624,25.319590716568648,-1.135216161881697],[0.6502230892315992,-1.135216161881697,28.002589075295308]],[[0.20772196929306083,-0.16035047001514102,0.6106797655515614],[-0.16035047001514105,26.54555785898669,-1.643554371696736],[0.6106797655515614,-1.643554371696736,28.157741030750508]]],"weights":[0.24391800630360175,0.2623161644960391,0.2516074177870681,0.242158411413291],"means":[[0.3093785189118171,98.50767974689373,12.04201267494459],[0.2957223232822461,100.29260369087528,11.368513289910661],[0.32869103266615624,100.24202740519003,12.299965535058508],[0.31513728410310543,100.08656506960568,12.162225242103984]]}]},
            "covarianceType":"full"
        },
    "message":"Training successfully! at Tue May 19 2015 15:42:52 GMT+0800 (CST)"}
    }
  

Train a event model randomly [/trainWithRandomObs/]

You can specify which model need training by tag. And all you need is giving the length of one observation and the count of observations, it will generate observations randomly and automaticly. The format of generated training sample is as same as above.

• Hint If you need store the result of training, you need add a key, named "description". The value of "description" is a memo of this train. And when you get the same tag model next time, you will get a whole new model params.

Train randomly or without observations [POST]

• Request (application/json)

  • Header

      X-AVOSCloud-Application-Id  : dkc5xdbwprsrh9809kqwopja5ckfbsrpd7dz9a30yugm9tut,
      X-AVOSCloud-Application-Key : 3sy9w8uwlr35xl54lja3rawyf8xjrhofxtvcwzng3blg7q31
    

  • Body

      {
      "algoType": "GMMHMM",
      "tag": "random_generated_base_model",
      "eventLabel": "dining.chineseRestaurant",
      "obsLength": 10,
      "obsCount": 500,
      "desciption": "test for api" (optional)
      }
    

• Response 201 (application/json)

    {
    "result":{
        "code":0,
        "model":{
            "nMix":4,
            "nComponent":4,
            "hmmParams":{
                "transMat":[
                    [0.9937524708135964,2.6823758143694116e-18,0.00624752918640371,2.6823758143694116e-18],[0.25,0.25,0.25,0.25],[3.5716227445160065e-17,3.5716227445160065e-17,0.9999999999999999,3.5716227445160065e-17],[0.25,0.25,0.25,0.25]
                ],
                "startProb":[1,2.2205460492503137e-17,2.2205460492503137e-17,2.2205460492503137e-17]
            },
            "gmmParams":{
                "nMix":4,
                "covarianceType":"full",
                "params":[
                    {"covars":[[[0.20272925448170726,-0.21372410825705002,0.6640076953697146],[-0.21372410825704996,44.41361612877133,-3.8315142667592568],[0.6640076953697147,-3.831514266759256,29.058072351569287]],[[0.19961779798118065,-0.20567068761490478,0.6180902066504756],[-0.2056706876149048,24.5623976379672,-1.8862253825381476],[0.6180902066504755,-1.886225382538147,28.57872805315829]],[[0.21297430879173704,-0.12933325429515627,0.6502230892315992],[-0.12933325429515624,25.319590716568648,-1.135216161881697],[0.6502230892315992,-1.135216161881697,28.002589075295308]],[[0.20772196929306083,-0.16035047001514102,0.6106797655515614],[-0.16035047001514105,26.54555785898669,-1.643554371696736],[0.6106797655515614,-1.643554371696736,28.157741030750508]]],"weights":[0.24391800630360175,0.2623161644960391,0.2516074177870681,0.242158411413291],"means":[[0.3093785189118171,98.50767974689373,12.04201267494459],[0.2957223232822461,100.29260369087528,11.368513289910661],[0.32869103266615624,100.24202740519003,12.299965535058508],[0.31513728410310543,100.08656506960568,12.162225242103984]]},{"covars":[[[0.20272925448170726,-0.21372410825705002,0.6640076953697146],[-0.21372410825704996,44.41361612877133,-3.8315142667592568],[0.6640076953697147,-3.831514266759256,29.058072351569287]],[[0.19961779798118065,-0.20567068761490478,0.6180902066504756],[-0.2056706876149048,24.5623976379672,-1.8862253825381476],[0.6180902066504755,-1.886225382538147,28.57872805315829]],[[0.21297430879173704,-0.12933325429515627,0.6502230892315992],[-0.12933325429515624,25.319590716568648,-1.135216161881697],[0.6502230892315992,-1.135216161881697,28.002589075295308]],[[0.20772196929306083,-0.16035047001514102,0.6106797655515614],[-0.16035047001514105,26.54555785898669,-1.643554371696736],[0.6106797655515614,-1.643554371696736,28.157741030750508]]],"weights":[0.24391800630360175,0.2623161644960391,0.2516074177870681,0.242158411413291],"means":[[0.3093785189118171,98.50767974689373,12.04201267494459],[0.2957223232822461,100.29260369087528,11.368513289910661],[0.32869103266615624,100.24202740519003,12.299965535058508],[0.31513728410310543,100.08656506960568,12.162225242103984]]},{"covars":[[[0.20272925448170726,-0.21372410825705002,0.6640076953697146],[-0.21372410825704996,44.41361612877133,-3.8315142667592568],[0.6640076953697147,-3.831514266759256,29.058072351569287]],[[0.19961779798118065,-0.20567068761490478,0.6180902066504756],[-0.2056706876149048,24.5623976379672,-1.8862253825381476],[0.6180902066504755,-1.886225382538147,28.57872805315829]],[[0.21297430879173704,-0.12933325429515627,0.6502230892315992],[-0.12933325429515624,25.319590716568648,-1.135216161881697],[0.6502230892315992,-1.135216161881697,28.002589075295308]],[[0.20772196929306083,-0.16035047001514102,0.6106797655515614],[-0.16035047001514105,26.54555785898669,-1.643554371696736],[0.6106797655515614,-1.643554371696736,28.157741030750508]]],"weights":[0.24391800630360175,0.2623161644960391,0.2516074177870681,0.242158411413291],"means":[[0.3093785189118171,98.50767974689373,12.04201267494459],[0.2957223232822461,100.29260369087528,11.368513289910661],[0.32869103266615624,100.24202740519003,12.299965535058508],[0.31513728410310543,100.08656506960568,12.162225242103984]]},{"covars":[[[0.20272925448170726,-0.21372410825705002,0.6640076953697146],[-0.21372410825704996,44.41361612877133,-3.8315142667592568],[0.6640076953697147,-3.831514266759256,29.058072351569287]],[[0.19961779798118065,-0.20567068761490478,0.6180902066504756],[-0.2056706876149048,24.5623976379672,-1.8862253825381476],[0.6180902066504755,-1.886225382538147,28.57872805315829]],[[0.21297430879173704,-0.12933325429515627,0.6502230892315992],[-0.12933325429515624,25.319590716568648,-1.135216161881697],[0.6502230892315992,-1.135216161881697,28.002589075295308]],[[0.20772196929306083,-0.16035047001514102,0.6106797655515614],[-0.16035047001514105,26.54555785898669,-1.643554371696736],[0.6106797655515614,-1.643554371696736,28.157741030750508]]],"weights":[0.24391800630360175,0.2623161644960391,0.2516074177870681,0.242158411413291],"means":[[0.3093785189118171,98.50767974689373,12.04201267494459],[0.2957223232822461,100.29260369087528,11.368513289910661],[0.32869103266615624,100.24202740519003,12.299965535058508],[0.31513728410310543,100.08656506960568,12.162225242103984]]}]},
            "covarianceType":"full"
        },
        "message":"Training successfully! at Tue May 19 2015 15:42:52 GMT+0800 (CST)"
    }
    }
  

Predict the event's type of a given behavior sequence [/classifySingleSeq/]

You can specify which serial of models used to predict by tag. It will return the scores of every possible event type.

Predict event's type of sequence [POST]

• Request (application/json)

  • Header

      X-AVOSCloud-Application-Id  : dkc5xdbwprsrh9809kqwopja5ckfbsrpd7dz9a30yugm9tut,
      X-AVOSCloud-Application-Key : 3sy9w8uwlr35xl54lja3rawyf8xjrhofxtvcwzng3blg7q31
    

  • Body

      {
      "algoType":"GMMHMM",
      "tag":"random_generated_base_model",
      "seq":[
          {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
          {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
          {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
          {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
          {"motion": "walking", "sound": "others", "location": "chinese_restaurant"},
          {"motion": "walking", "sound": "tableware", "location": "chinese_restaurant"},
          {"motion": "sitting", "sound": "laugh", "location": "chinese_restaurant"},
          {"motion": "sitting", "sound": "talking", "location": "chinese_restaurant"},
          {"motion": "sitting", "sound": "tableware", "location": "residence"},
          {"motion": "sitting", "sound": "others", "location": "glass_store"}]
      }
    

• Response 201 (application/json)

    {
    "result":{
        "code":0,
        "scores":{
            "dining.chineseRestaurant":-67.08776698008978
        },
        "message":"Classifying successfully! at Tue May 19 2015 16:33:48 GMT+0800 (CST)"
    }
    }
  

Init a Model for a event [/initModelParams/]

You can init a model params with tag, event label and algo type.

Init Model [POST]

• Request (application/json)

  • Header

      X-AVOSCloud-Application-Id  : dkc5xdbwprsrh9809kqwopja5ckfbsrpd7dz9a30yugm9tut,
      X-AVOSCloud-Application-Key : 3sy9w8uwlr35xl54lja3rawyf8xjrhofxtvcwzng3blg7q31
    

  • Body

      {
      "algoType": "GMMHMM",
      "tag": "random_generated_base_model",
      "eventLabel": "dining.chineseRestaurant"
      }
    

• Response 201 (application/json)

    {
    "result":{
        "code":0,
        "modelId":"555c8fe2e4b044c3499f2d2d",
        "message":"Model init successfully! at Wed May 20 2015 21:45:06 GMT+0800 (CST)"
        }
    }