我正在尝试使用LinearRegressionWithSGD构建一个简单的线性模型来预测标签值。 我转换数据集以获取要素和标签，然后再次转换为标记点进行回归
final ImageReader.OnImageAvailableListener readerListener = new ImageReader.OnImageAvailableListener() {
@Override
public void onImageAvailable(ImageReader reader) {
Image image = null;
try {
image = reader.acquireLatestImage();
ByteBuffer buffer = image.getPlanes()[0].getBuffer();
byte[] bytes = new byte[buffer.capacity()];
buffer.get(bytes);
save(bytes);
mBitmapToSave1 = BitmapFactory.decodeByteArray(bytes, 0, bytes.length);
mBitmapToSave = BitmapFactory.decodeByteArray(bytes, 0, bytes.length);
Bitmap scaled = Bitmap.createScaledBitmap(mBitmapToSave, width, height, true);
int w = scaled.getWidth();
int h = scaled.getHeight();
// Setting post rotate to 90
Matrix mtx = new Matrix();
mtx.postRotate(-180);
// Rotating Bitmap
mBitmapToSave = Bitmap.createBitmap(scaled, 0, 0, w, h, mtx, true);
// mBitmapToSave = Bitmap.createBitmap(width+rowPadding/pixelStride,height, Bitmap.Config.RGB_565);
// mBitmapToSave.copyPixelsToBuffer(buffer);
if (detector.isOperational() && mBitmapToSave != null) {
Frame frame = new Frame.Builder().setBitmap(mBitmapToSave).build();
SparseArray<Face> faces = detector.detect(frame);
for (index = 0; index < faces.size(); ++index) {
Face face = faces.valueAt(index);
}if (faces.size() == 0) {
MediaPlayer mediaPlayer = MediaPlayer.create(getApplicationContext(), R.raw.not);
mediaPlayer.start();
//Toast.makeText(AndroidCamera2API.this, "Face Not detected Adjust Camera Properly", Toast.LENGTH_SHORT).show();
} else {
Toast.makeText(AndroidCamera2API.this, "Face Found " + "\n", Toast.LENGTH_SHORT).show();
}
}
}catch(FileNotFoundException e){
e.printStackTrace();
} catch(IOException e){
e.printStackTrace();
} finally{
if (image != null) {
image.close();
}
}
}
private void save(byte[] bytes) throws IOException {
OutputStream output = null;
try {
output = new FileOutputStream(file);
output.write(bytes);
} finally {
if (null != output) {
output.close();
}
}
}
};
reader.setOnImageAvailableListener(readerListener, mBackgroundHandler);
final CameraCaptureSession.CaptureCallback captureListener = new CameraCaptureSession.CaptureCallback() {
@Override
public void onCaptureCompleted(CameraCaptureSession session, CaptureRequest request, TotalCaptureResult result) {
super.onCaptureCompleted(session, request, result);
Toast.makeText(AndroidCamera2API.this, "Saved:" + file, Toast.LENGTH_SHORT).show();
uploadMultipart();
createCameraPreview();
}
}
};
cameraDevice.createCaptureSession(outputSurfaces, new CameraCaptureSession.StateCallback() {
@Override
public void onConfigured(CameraCaptureSession session) {
try {
session.capture(captureBuilder.build(), captureListener, mBackgroundHandler);
} catch (CameraAccessException e) {
e.printStackTrace();
}
}
@Override
public void onConfigureFailed(CameraCaptureSession session) {
}
}, mBackgroundHandler);
mBitmapToSave = null;
} catch(CameraAccessException e){
e.printStackTrace();
}
}
现在我正在使用模型
val train = dftrain.withColumn("label", dftrain("col2")).select("features", "label")
val test = dftest.withColumn("label", dftest("col2")).select("features", "label")
val realout = train.rdd.map(row => LabeledPoint(row.getAs[Double]("label"),DenseVector.fromML(row.getAs[org.apache.spark.ml.linalg.DenseVector]("features"))))
val realout1 = test.rdd.map(row => LabeledPoint(row.getAs[Double]("label"),DenseVector.fromML(row.getAs[org.apache.spark.ml.linalg.DenseVector]("features"))))
val numIterations = 100 val stepSize = 0.00000001 //fitting the model with converted Labeled points Train Data val model = LinearRegressionWithSGD.train(realout, numIterations, stepSize)
它给了我一些警告，并且它给 17/08/09 12:16:15 WARN LinearRegressionWithSGD: The input data is not directly c
ached, which may hurt performance if its parent RDDs are also uncached.
17/08/09 12:16:17 WARN BLAS: Failed to load implementation from: com.github.fomm
il.netlib.NativeSystemBLAS
17/08/09 12:16:17 WARN BLAS: Failed to load implementation from: com.github.fomm
il.netlib.NativeRefBLAS
17/08/09 12:16:17 WARN LinearRegressionWithSGD: The input data was not directly
cached, which may hurt performance if its parent RDDs are also uncached.
model: org.apache.spark.mllib.regression.LinearRegressionModel = org.apache.spar
k.mllib.regression.LinearRegressionModel: intercept = 0.0, numFeatures = 1
为0.0，我感觉不正确。但是当我预测模型时，它会让我错误。
Intercept此外，如果我从here，
执行此操作val prediction = model.predict(realout1)
<console>:98: error: overloaded method value predict with alternatives:
(testData: org.apache.spark.api.java.JavaRDD[org.apache.spark.mllib.linalg.Vec
tor])org.apache.spark.api.java.JavaRDD[Double] <and>
(testData: org.apache.spark.mllib.linalg.Vector)Double <and>
(testData: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.Vector])org.
apache.spark.rdd.RDD[Double]
cannot be applied to (org.apache.spark.rdd.RDD[org.apache.spark.mllib.regressio
n.LabeledPoint])
val prediction = model.predict(realout1)
^
我相信这些步骤是正确的。但我不知道为什么它显示重载方法值预测与替代或 Double不接受参数
答案 0 :(得分：0)
val prediction = model.predict(realout1.map(_.features));
这个工作正常。但我不知道这个是多么正确。任何建议表示赞赏。谢谢。