emlearn · jonnor · Oct 18, 2024 · Oct 15, 2024 · Oct 15, 2024 · Oct 18, 2024
diff --git a/Makefile b/Makefile
@@ -10,49 +10,49 @@ MPY_DIR_ABS = $(abspath $(MPY_DIR))
 
 MODULES_PATH = ./dist/$(ARCH)_$(MPY_ABI_VERSION)
 
-$(MODULES_PATH)/emltrees.mpy:
-	make -C src/emltrees/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
+$(MODULES_PATH)/emlearn_trees.mpy:
+	make -C src/emlearn_trees/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
 
-$(MODULES_PATH)/emlneighbors.mpy:
-	make -C src/emlneighbors/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
+$(MODULES_PATH)/emlearn_neighbors.mpy:
+	make -C src/emlearn_neighbors/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
 
-$(MODULES_PATH)/emliir.mpy:
-	make -C src/emliir/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
+$(MODULES_PATH)/emlearn_iir.mpy:
+	make -C src/emlearn_iir/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
 
-$(MODULES_PATH)/emlfft.mpy:
-	make -C src/emlfft/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
+$(MODULES_PATH)/emlearn_fft.mpy:
+	make -C src/emlearn_fft/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
 
-$(MODULES_PATH)/tinymaix_cnn.mpy:
+$(MODULES_PATH)/emlearn_cnn.mpy:
 	make -C src/tinymaix_cnn/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
 
-$(MODULES_PATH)/emlkmeans.mpy:
-	make -C src/emlkmeans/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
+$(MODULES_PATH)/emlearn_kmeans.mpy:
+	make -C src/emlearn_kmeans/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
 
-$(MODULES_PATH)/eml_iir_q15.mpy:
-	make -C src/eml_iir_q15/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
+$(MODULES_PATH)/emlearn_iir_q15.mpy:
+	make -C src/emlearn_iir_q15/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
 
 $(MODULES_PATH)/emlearn_arrayutils.mpy:
 	make -C src/emlearn_arrayutils/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
 
-emltrees.results: $(MODULES_PATH)/emltrees.mpy
+emlearn_trees.results: $(MODULES_PATH)/emlearn_trees.mpy
 	MICROPYPATH=$(MODULES_PATH) $(MICROPYTHON_BIN) tests/test_trees.py
 
-emlneighbors.results: $(MODULES_PATH)/emlneighbors.mpy
+emlearn_neighbors.results: $(MODULES_PATH)/emlearn_neighbors.mpy
 	MICROPYPATH=$(MODULES_PATH) $(MICROPYTHON_BIN) tests/test_neighbors.py
 
-emliir.results: $(MODULES_PATH)/emliir.mpy
+emlearn_iir.results: $(MODULES_PATH)/emlearn_iir.mpy
 	MICROPYPATH=$(MODULES_PATH) $(MICROPYTHON_BIN) tests/test_iir.py
 
-emlfft.results: $(MODULES_PATH)/emlfft.mpy
+emlearn_fft.results: $(MODULES_PATH)/emlearn_fft.mpy
 	MICROPYPATH=$(MODULES_PATH) $(MICROPYTHON_BIN) tests/test_fft.py
 
-tinymaix_cnn.results: $(MODULES_PATH)/tinymaix_cnn.mpy
+emlearn_cnn.results: $(MODULES_PATH)/emlearn_cnn.mpy
 	MICROPYPATH=$(MODULES_PATH) $(MICROPYTHON_BIN) tests/test_cnn.py
 
-emlkmeans.results: $(MODULES_PATH)/emlkmeans.mpy
+emlearn_kmeans.results: $(MODULES_PATH)/emlearn_kmeans.mpy
 	MICROPYPATH=$(MODULES_PATH) $(MICROPYTHON_BIN) tests/test_kmeans.py
 
-eml_iir_q15.results: $(MODULES_PATH)/eml_iir_q15.mpy
+emlearn_iir_q15.results: $(MODULES_PATH)/emlearn_iir_q15.mpy
 	MICROPYPATH=$(MODULES_PATH) $(MICROPYTHON_BIN) tests/test_iir_q15.py
 
 emlearn_arrayutils.results: $(MODULES_PATH)/emlearn_arrayutils.mpy
@@ -61,9 +61,9 @@ emlearn_arrayutils.results: $(MODULES_PATH)/emlearn_arrayutils.mpy
 .PHONY: clean
 
 clean:
-	make -C src/emltrees/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean
-	make -C src/emlneighbors/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean
-	make -C src/emliir/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean
+	make -C src/emlearn_trees/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean
+	make -C src/emlearn_neighbors/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean
+	make -C src/emlearn_iir/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean
 	rm -rf ./dist
 
 RELEASE_NAME = emlearn-micropython-$(VERSION)
@@ -74,8 +74,8 @@ release:
 	zip -r $(RELEASE_NAME).zip $(RELEASE_NAME)
 	#cp $(RELEASE_NAME).zip emlearn-micropython-latest.zip
 
-check: emltrees.results emlneighbors.results emliir.results eml_iir_q15.results emlfft.results emlkmeans.results emlearn_arrayutils.results tinymaix_cnn.results
+check: emlearn_trees.results emlearn_neighbors.results emlearn_iir.results emlearn_iir_q15.results emlearn_fft.results emlearn_kmeans.results emlearn_arrayutils.results emlearn_cnn.results
 
-dist: $(MODULES_PATH)/emltrees.mpy $(MODULES_PATH)/emlneighbors.mpy $(MODULES_PATH)/emliir.mpy $(MODULES_PATH)/eml_iir_q15.mpy $(MODULES_PATH)/emlfft.mpy $(MODULES_PATH)/emlkmeans.mpy $(MODULES_PATH)/emlearn_arrayutils.mpy $(MODULES_PATH)/tinymaix_cnn.mpy
+dist: $(MODULES_PATH)/emlearn_trees.mpy $(MODULES_PATH)/emlearn_neighbors.mpy $(MODULES_PATH)/emlearn_iir.mpy $(MODULES_PATH)/emlearn_iir_q15.mpy $(MODULES_PATH)/emlearn_fft.mpy $(MODULES_PATH)/emlearn_kmeans.mpy $(MODULES_PATH)/emlearn_arrayutils.mpy $(MODULES_PATH)/emlearn_cnn.mpy
 
 
diff --git a/README.md b/README.md
@@ -57,10 +57,21 @@ Download the repository with examples etc
 git clone https://github.com/emlearn/emlearn-micropython
 ```
 
-## Installing from a release
+## Usage
+
+Start with the instructions in [XOR example](./examples/xor_trees/).
+
 
 #### Find architecture and .mpy version
 
+The correct .mpy files to use depend on the CPU architecture of your microcontroller,
+as well as the MicroPython version.
+
+| MicroPython version | .mpy version  |
+|---------------------| ------------- |
+| 1.23.x              | 6.3           |
+
+
 Identify which CPU architecture your device uses.
 You need to specify `ARCH` to install the correct module version.
 
@@ -79,43 +90,6 @@ Information is also available in the official documentation:
 [MicroPython: .mpy files](https://docs.micropython.org/en/latest/reference/mpyfiles.html#versioning-and-compatibility-of-mpy-files)
 
 
-#### Download release files
-
-Download from [releases](https://github.com/emlearn/emlearn-micropython/releases).
-
-#### Install on device
-
-Copy the .mpy file for the correct `ARCH` to your device.
-```
-mpremote cp emltrees.mpy :emltrees.mpy
-mpremote cp emlneighbors.mpy :emlneighbors.mpy
-```
-
-NOTE: If there is no ready-made build for your device/architecture,
-then you will need to build the .mpy module yourself.
-
-## Usage
-
-NOTE: Make sure to install the module first (see above)
-
-Train a model with scikit-learn
-```
-pip install emlearn scikit-learn
-python examples/xor_trees/xor_train.py
-```
-
-Copy model file to device
-
-```
-mpremote cp xor_model.csv :xor_model.csv
-```
-
-Run program that uses the model
-
-```
-mpremote run examples/xor_run.py
-```
-
 ## Benchmarks
 
 #### UCI handwriting digits
@@ -157,7 +131,7 @@ make dist ARCH=armv6m MPY_DIR=../micropython
 
 Install it on device
 ```
-mpremote cp dist/armv6m*/emltrees.mpy :emltrees.mpy
+mpremote cp dist/armv6m*/emlearn_trees.mpy :emlearn_trees.mpy
 ```
 
 #### Run tests

diff --git a/benchmarks/digits_trees/digits_run.py b/benchmarks/digits_trees/digits_run.py
@@ -6,15 +6,15 @@
 
 from everywhere_digits import RandomForestClassifier
 import m2c_digits
-import emltrees
+import emlearn_trees
 
 
 def emlearn_create():
-    model = emltrees.new(10, 1000, 10)
+    model = emlearn_trees.new(10, 1000, 10)
 
     # Load a CSV file with the model
     with open('eml_digits.csv', 'r') as f:
-        emltrees.load_model(model, f)
+        emlearn_trees.load_model(model, f)
     return model
 
 def argmax(l):

diff --git a/benchmarks/fft/fft_benchmark.py b/benchmarks/fft/fft_benchmark.py
@@ -16,9 +16,9 @@
 except ImportError as e:
     print(e)
 
-emlfft = None
+emlearn_fft = None
 try:
-    import emlfft
+    import emlearn_fft
     pass
 except ImportError as e:
     print(e)
@@ -65,13 +65,13 @@ def run_one(real, imag, n, repeat=10):
         d = ((time.ticks_diff(time.ticks_us(), start)) / repeat) / 1000.0 # ms
         print('ulab', n, d)
 
-    # FIXME: this causes MicroPython to crash inside emlfft on ESP32
+    # FIXME: this causes MicroPython to crash inside emlearn_fft on ESP32
     #gc.collect()
 
     # emlearn
     if emlearn:        
-        fft2 = emlfft.FFT(n)
-        emlfft.fill(fft2, n)
+        fft2 = emlearn_fft.FFT(n)
+        emlearn_fft.fill(fft2, n)
         gc.collect()
 
         start = time.ticks_us()

diff --git a/benchmarks/fft/fft_benchmark.sh b/benchmarks/fft/fft_benchmark.sh
@@ -1,9 +1,9 @@
 # NOTE: MicroPython must be flashed before-hand
-# and emlfft.mpy built
+# and emlearn_fft.mpy built
 
 MPREMOTE='mpremote'
 
-${MPREMOTE} cp src/emlfft/emlfft.mpy :
+${MPREMOTE} cp src/emlearn_fft/emlearn_fft.mpy :
 ${MPREMOTE} cp benchmarks/fft/fft_python.py :
 
 ${MPREMOTE} run benchmarks/fft/fft_benchmark.py

diff --git a/benchmarks/iir/iir_benchmark.py b/benchmarks/iir/iir_benchmark.py
@@ -14,9 +14,9 @@
     print(e)
     pass
 
-emliir = None
+emlearn_iir = None
 try:
-    import emliir
+    import emlearn_iir
 except ImportError as e:
     print(e)
     pass
@@ -70,9 +70,9 @@ def main():
         print('ulab', t)
 
     # emlearn
-    if emliir:
+    if emlearn_iir:
         start = time.ticks_us()
-        iir = emliir.new(coeff)
+        iir = emlearn_iir.new(coeff)
         for r in range(repeats):
             iir.run(a)
         t = (time.ticks_diff(time.ticks_us(), start) / repeats ) / 1000.0 # ms

diff --git a/benchmarks/iir/iir_run.py b/benchmarks/iir/iir_run.py
@@ -27,11 +27,11 @@ def iir_process_file(inp, out, filters, chunksize):
         if len(reader.shape) != 1:
             raise ValueError("Input must be 1d")
         if reader.typecode == 'f':
-            import emliir
-            filter = emliir.new(coefficients)
+            import emlearn_iir
+            filter = emlearn_iir.new(coefficients)
         elif reader.typecode == 'h':
-            import eml_iir_q15
-            filter = eml_iir_q15.new(coefficients)
+            import emlearn_iir_q15
+            filter = emlearn_iir_q15.new(coefficients)
         else:
             raise ValueError("Input must either be float32/f or int16/h")
 

diff --git a/doc/TODO.md b/doc/TODO.md
@@ -3,7 +3,8 @@
 # User journey
 
 - Level 0a. Run a pretrained example/demo in the browser
-- Level 0b. Run a pretrained example/demo on a board
+- Level 0b. Run a pretrained example/demo on PC/host
+- Level 0c. Run a pretrained example/demo on a board
 - Level 1. Train custom model on-device
 - Level 2. Collect a dataset, do training on PC, deploy back to microcontroller
 - Level 3. Bake the custom model into the firmware
@@ -20,33 +21,21 @@
 
 sequence. On-device training demo
 
-- Record piezo data with ADC. 100 Hz?
-Typical taps. Slower pushes. Handling noises.
-- Setup event detection for piezo.
-In its own module.
-Threshold on delta and level?
-- Create emliir module, use for piezo detection
+- Use accelerometer instead of piezo. On M5StickC, for example
+- Compute impulsive-ness feature. Magnitude, RMS, exponential smooth, then Delta * times level ?
+- Alternative: Use IIR for knock detection
 - Maybe blink during unlocked state
 - Add a blink to each event. For user feedback
 - Make demo video
+- Add some documentation / README
 - Make state diagram
 - Make timing diagram. Highlight distances/features
-- Add some documentation / README
-
-Learnings.
-
-- Putting piezo on small thin plate worked well.
-On table not working, no response.
-Hitting direct not so good either, rise of finger causes change. Double-trigger. Also tricky to hit in right place.
-- LEDs as protection diodes worked well. Both red and green can be used. Lights up on direct hits, if placed by piezo.
-- Analog RC filter is beneficial for piezo connections. Using 10k+100nF, has 160 Hz cutoff. Should maybe move it to 80Hz? Since only sampling at 100 Hz. 
-- Only direct hits can reach trigger levels on 3.3V I/O. Need ADC for other cases. But am seing some 100mV when placed on small plate
 
-Examples
+#### Examples
 
 - Add a novelty detection example?
 
-Benchmarks
+#### Benchmarks
 
 - Add FLASH and RAM usage
 - Test gzip compression of .csv model for trees
@@ -56,3 +45,5 @@ Benchmarks
 In-browser demo
 
 - Test MicroPython build for WASM/browser
+- Test getting audio input into MicroPython Webassembly
+- Test getting IMU data (ie on phone), in browser
diff --git a/doc/micropython-tinyml-status.md b/doc/micropython-tinyml-status.md
@@ -167,14 +167,14 @@ FIR filters.
 
 IIR filters.
 `scipy.signal.sosfilt` available in [ulab](https://github.com/v923z/micropython-ulab).
-`emliir` available in [emlearn-micropython](https://github.com/emlearn/emlearn-micropython).
+`emlearn_iir` available in [emlearn-micropython](https://github.com/emlearn/emlearn-micropython).
 
 ### Fast Fourier Transform (FFT)
 Key part of computing frequency spectrum, or time-frequency representations (spectrogram).
 
 FFT.
 `numpy.fft.fft` available in [ulab](https://github.com/v923z/micropython-ulab).
-`emlfft` available in [emlearn-micropython](https://github.com/emlearn/emlearn-micropython).
+`emlearn_fft` available in [emlearn-micropython](https://github.com/emlearn/emlearn-micropython).
 
 DCT.
 Not available?
@@ -195,7 +195,7 @@ But over 10x slower than emlearn-micropython.
 
 ### K-nearest-neighbours
 
-`emlneighbors` available in [emlearn-micropython](https://github.com/emlearn/emlearn-micropython).
+`emlearn_neighbors` available in [emlearn-micropython](https://github.com/emlearn/emlearn-micropython).
 
 ### Convolutional Neural Network
 

diff --git a/examples/color_quantize_kmeans/color_quantize.py b/examples/color_quantize_kmeans/color_quantize.py
@@ -9,7 +9,7 @@
 import os
 import gc
 
-import emlkmeans
+import emlearn_kmeans
 
 @micropython.native
 def quantize_image(img, quant, palette, rowstride):
@@ -55,7 +55,7 @@ def quantize_image_inner(img, quant, palette, rowstride : int, rows : int):
             #rgb = img[i:i+3]
 
             # find closest value in palette
-            palette_idx, distance = emlkmeans.euclidean_argmin(palette, rgb)
+            palette_idx, distance = emlearn_kmeans.euclidean_argmin(palette, rgb)
             #palette_idx, distance = 0, 0
 
             o = row_offset + col
@@ -103,7 +103,7 @@ def quantize_path(inp, outp, palette, n_samples=100):
 
     # Learn a palette
     start = time.ticks_us()
-    emlkmeans.cluster(samples, palette, features=3, max_iter=20)
+    emlearn_kmeans.cluster(samples, palette, features=3, max_iter=20)
     dur = (time.ticks_diff(time.ticks_us(), start) / 1000.0)
     print('cluster duration (ms)', dur)