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jubaclient

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jubaclient - npm Package Compare versions

Comparing version 0.1.0 to 0.1.1

2

package.json
{
"name": "jubaclient",
"version": "0.1.0",
"version": "0.1.1",
"description": "Jubatus CLI client (unofficial)",

@@ -5,0 +5,0 @@ "main": "index.js",

@@ -17,9 +17,13 @@ # jubaclient

# startup jubaclassifier
jubaclassifier -p 9190 -f ./config.json -D
jubaclassifier -f ./config.json -D
# classifier#train()
echo '[ [ [ "baz", [ [ [ "foo", "bar" ] ] ] ] ] ]' | \
jubaclient classifier train 9190 localhost 10 | jq '.'
jubaclient classifier train | jq '.'
```
## Requires ##
- [Node.js](https://nodejs.or]) v6+
## Installation ##

@@ -35,3 +39,6 @@

The `jubaclient` command requests JSON received from standard input with the specified method to the Jubatus server, and returns the response to the standard output.
The command line options are as follows:
- <code>_service_</code>: sevice name (`classifier`, `nearest_neighbor`, etc.)

@@ -46,11 +53,11 @@ - <code>_method_</code>: service method (`get_status`, `train`, `get_k_center`, etc.)

- save(id)
- #save(id)
```bash
echo '[ "jubaclient_save_1" ]' | jubaclient classifier save
```
- get_status()
- #get_status()
```bash
echo '[]' | jubaclient classifier get_status | jq '.'
```
- get_config()
- #get_config()
```bash

@@ -75,5 +82,64 @@ echo '[]' | jubaclient classifier get_config | jq '.|fromjson'

```
## Tutorial ##
## Requires ##
### Classifier ####
- [Node.js](https://nodejs.or]) v6+
See also http://jubat.us/en/tutorial/classifier.html
1. start `jubaclassifier` process.
```bash
jubaclassifier -D --configpath gender.json
```
2. train
```bash
cat train.csv \
| jq -RcM 'split(",")|[[[.[0],[[["hair",.[1]],["top",.[2]],["bottom",.[3]]],[["height",(.[4]|tonumber)]]]]]]' \
| jubaclient classifier train
```
3. classify
```bash
cat classify.csv \
| jq -RcM 'split(",")|[[[[["hair",.[0]],["top",.[1]],["bottom",.[2]]],[["height",(.[3]|tonumber)]]]]]' \
| jubaclient classifier classify \
| jq '.[]|max_by(.[1])'
```
configure: `gender.json`
```json
{
"method": "AROW",
"converter": {
"num_filter_types": {}, "num_filter_rules": [],
"string_filter_types": {}, "string_filter_rules": [],
"num_types": {}, "num_rules": [],
"string_types": {
"unigram": { "method": "ngram", "char_num": "1" }
},
"string_rules": [
{ "key": "*", "type": "unigram", "sample_weight": "bin", "global_weight": "bin" }
]
},
"parameter": { "regularization_weight" : 1.0 }
}
```
training data: `train.csv`
```csv
male,short,sweater,jeans,1.70
female,long,shirt,skirt,1.56
male,short,jacket,chino,1.65
female,short,T shirt,jeans,1.72
male,long,T shirt,jeans,1.82
female,long,jacket,skirt,1.43
```
test data: `classify.csv`
```csv
short,T shirt,jeans,1.81
long,shirt,skirt,1.50
```
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