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+# Machine learning demo using iris dataset
+
+### Classification task demo, tested on stm32f4 and k210-based edge devices. Training on iris dataset by *Decision Tree classifier*, *Support Vector Machine classifier* and *Logistic Regression classifier*.
+
+---
+
+## Training
+
+Model generated by [Sklearn](https://scikit-learn.org/stable/) and converted to C language by [micromlgen](https://forgeplus.trustie.net/projects/yangtuo250/micromlgen).
+
+### Enviroment preparation
+
+```shell
+pip install scikit-learn
+git clone https://git.trustie.net/yangtuo250/micromlgen.git -b C
+cd micromlgen && pip install -e .
+```
+
+### Train it!
+
+```python
+# load iris dataset
+from sklearn.datasets import load_iris
+X, y = load_iris(return_X_y=True)
+
+# train SVC classifier and convert
+clf = SVC(kernel='linear', gamma=0.001).fit(X, y)
+print(port(clf, cplusplus=False, platform=platforms.STM32F4))
+
+# train logistic regression classifier and convert
+clf = LogisticRegression(max_iter=1000).fit(X, y)
+print(port(clf, cplusplus=False, platform=platforms.STM32F4))
+
+# train decision tree classifier and convert
+clf = DecisionTreeClassifier().fit(X, y)
+print(port(clf, cplusplus=False, platform=platforms.STM32F4)
+```
+Copy each content generated by print to a single C language file.
+
+---
+
+## Deployment
+
+### compile and burn
+
+Use `(scons --)menuconfig` in *bsp folder(Ubiquitous/RT_Thread/bsp/k210(or stm32f407-atk-coreboard))*, open **APP_Framwork --> Applications --> knowing app --> enable apps/iris ml demo** to enable this app. `scons -j(n)` to compile and burn in by *st-flash(for ARM)* or *kflash(for k210)*.
+
+### testing set
+
+Copy *iris.csv* to SD card */csv/iris.csv*.
+
+---
+
+## Run
+
+In serial terminal:  
+- `iris_SVC_predict` for SVC prediction
+- `iris_DecisonTree_predict` for decision tree prediction
+- `iris_LogisticRegression_predict` for logistic regression prediction
+
+Example output:
+
+```shell
+data 1: 5.1000 3.5000 1.4000 0.2000 result: 0
+data 2: 6.4000 3.2000 4.5000 1.5000 result: 1
+data 3: 5.8000 2.7000 5.1000 1.9000 result: 2
+data 4: 7.7000 3.8000 6.7000 2.2000 result: 2
+data 5: 5.5000 2.6000 4.4000 1.2000 result: 1
+data 6: 5.1000 3.8000 1.9000 0.4000 result: 0
+data 7: 5.8000 2.7000 3.9000 1.2000 result: 1
+```