使用Genuino 101进行机器学习

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#include "CurieIMU.h"
#include "CuriePME.h"

const unsigned int buttonPin = 4;
const unsigned int trainingReps = 4;
const unsigned int classification = 3;
const unsigned int len = 128;

void training()
{
    unsigned int currentClassification = 0;
    while ( currentClassification < classification ) {
        currentClassification++;

        unsigned int currentTraining = 0;
        while( currentTraining < trainingReps ) {
            currentTraining++;
            // 获取传感器数据
            byte data[128] = readVectorFromIMU(vector);
            // 使用该数据进行训练
            CuriePME.learn(vector, 128, currentClassification);
        }






    }

    unsigned int currentTraining = 0;
    while (i < repeat) {
        byte vector[vectorNumBytes];

        if (i) Serial.println("And again...");

        readVectorFromIMU(vector);
        CuriePME.learn(vector, vectorNumBytes, letter - upperStart);

        Serial.println("Got it!");
        delay(1000);
        ++i;
    }
}


/* Sample rate for accelerometer */
const unsigned int sampleRateHZ = 200;

/* No. of bytes that one neuron can hold */
const unsigned int vectorNumBytes = 128;

/* Number of processed samples (1 sample == accel x, y, z)
 * that can fit inside a neuron */
const unsigned int samplesPerVector = (vectorNumBytes / 3);


const unsigned int sensorBufSize = 2048;
const int IMULow = -32768;
const int IMUHigh = 32767;

void setup()
{
    Serial.begin(9600);
    while(!Serial);
    pinMode(buttonPin, INPUT);
    CurieIMU.begin();
    CuriePME.begin();
    CurieIMU.setAccelerometerRate(sampleRateHZ);
    CurieIMU.setAccelerometerRange(2);
    // 进入训练状态
    trainLetters();
    Serial.println("Training complete. Now, draw some letters (remember to ");
    Serial.println("hold the button) and see if the PME can classify them.");
}

void loop ()
{
    // 进入识别状态
    byte vector[vectorNumBytes];
    unsigned int category;
    char letter;

    /* Record IMU data while button is being held, and
     * convert it to a suitable vector */
    readVectorFromIMU(vector);

    /* Use the PME to classify the vector, i.e. return a category
     * from 1-26, representing a letter from A-Z */
    category = CuriePME.classify(vector, vectorNumBytes);

    if (category == CuriePME.noMatch) {
        Serial.println("Don't recognise that one-- try again.");
    } else {
        letter = category + upperStart;
        Serial.println(letter);
    }
}

/* Simple "moving average" filter, removes low noise and other small
 * anomalies, with the effect of smoothing out the data stream. */
byte getAverageSample(byte samples[], unsigned int num, unsigned int pos,
                   unsigned int step)
{
    unsigned int ret;
    unsigned int size = step * 2;

    if (pos < (step * 3) || pos > (num * 3) - (step * 3)) {
        ret = samples[pos];
    } else {
        ret = 0;
        pos -= (step * 3);
        for (unsigned int i = 0; i < size; ++i) {
            ret += samples[pos - (3 * i)];
        }

        ret /= size;
    }

    return (byte)ret;
}

/* We need to compress the stream of raw accelerometer data into 128 bytes, so
 * it will fit into a neuron, while preserving as much of the original pattern
 * as possible. Assuming there will typically be 1-2 seconds worth of
 * accelerometer data at 200Hz, we will need to throw away over 90% of it to
 * meet that goal!
 *
 * This is done in 2 ways:
 *
 * 1. Each sample consists of 3 signed 16-bit values (one each for X, Y and Z).
 *    Map each 16 bit value to a range of 0-255 and pack it into a byte,
 *    cutting sample size in half.
 *
 * 2. Undersample. If we are sampling at 200Hz and the button is held for 1.2
 *    seconds, then we'll have ~240 samples. Since we know now that each
 *    sample, once compressed, will occupy 3 of our neuron's 128 bytes
 *    (see #1), then we know we can only fit 42 of those 240 samples into a
 *    single neuron (128 / 3 = 42.666). So if we take (for example) every 5th
 *    sample until we have 42, then we should cover most of the sample window
 *    and have some semblance of the original pattern. */
void undersample(byte samples[], int numSamples, byte vector[])
{
    unsigned int vi = 0;
    unsigned int si = 0;
    unsigned int step = numSamples / samplesPerVector;
    unsigned int remainder = numSamples - (step * samplesPerVector);

    /* Centre sample window */
    samples += (remainder / 2) * 3;
    for (unsigned int i = 0; i < samplesPerVector; ++i) {
        for (unsigned int j = 0; j < 3; ++j) {
            vector[vi + j] = getAverageSample(samples, numSamples, si + j, step);
        }

        si += (step * 3);
        vi += 3;
    }
}

void readVectorFromIMU(byte vector[])
{
    byte accel[sensorBufSize];
    int raw[3];

    unsigned int samples = 0;
    unsigned int i = 0;

    /* Wait until button is pressed */
    while (digitalRead(buttonPin) == LOW);

    /* While button is being held... */
    while (digitalRead(buttonPin) == HIGH) {
        if (CurieIMU.accelDataReady()) {

            CurieIMU.readAccelerometer(raw[0], raw[1], raw[2]);
            accel[i] = (byte) map(raw[0], IMULow, IMUHigh, 0, 255);
            accel[i + 1] = (byte) map(raw[1], IMULow, IMUHigh, 0, 255);
            accel[i + 2] = (byte) map(raw[2], IMULow, IMUHigh, 0, 255);
            i += 3;
            ++samples;
            /* If there's not enough room left in the buffers
            * for the next read, then we're done */
            if (i + 3 > sensorBufSize) {
                break;
            }
        }
    }

    undersample(accel, samples, vector);
}

void trainLetter(char letter, unsigned int repeat)
{
    unsigned int i = 0;

    while (i < repeat) {
        byte vector[vectorNumBytes];

        if (i) Serial.println("And again...");

        readVectorFromIMU(vector);
        CuriePME.learn(vector, vectorNumBytes, letter - upperStart);

        Serial.println("Got it!");
        delay(1000);
        ++i;
    }
}

void trainLetters()
{
    for (char i = trainingStart; i <= trainingEnd; ++i) {
        Serial.print("Hold down the button and draw the letter '");
        Serial.print(String(i) + "' in the air. Release the button as soon ");
        Serial.println("as you are done.");

        trainLetter(i, trainingReps);
        Serial.println("OK, finished with this letter.");
        delay(2000);
    }
}


 

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