haifengl on master
fix test data path using file separator Merge pull request #706 from ra… (compare)
haifengl on master
update NNS usage (compare)
haifengl on master
refactor LinearSearch<K,V> refactor BKTree<K,V> refactor CoverTree<K,V> (compare)
haifengl on master
revise javadoc refine NNS javadoc and tests (compare)
haifengl on master
merge NearestNeighborSearch int… (compare)
The response variable Classification doesn't exist in the schema [...]
. Should I pay attention to this message? How do I get rid of it?
sterr = true
smile.validation.metric
package. The classification algorithms report posteriori probabilities if they are SoftClassifier
. Some algorithms also have a score()
method, which is not necessarily probabilities though.
Hi, I have a question about the new(?) OLS.fit() method. If I try to use it to predict housing sale prices, like OLS.fit(Formula.lhs("SalePrice"), X_train_dataframe), it fails with "no response variable".
So it seems I can only use OLS if I pass it a dataframe that includes X and y together. However, if I use it like "OLS.fit(Formula.lhs("SalePrice"), training_dataframe), which includes both the dependent and independent columns (X and y), then predict requires an array of the same size, including a column for the value I wish to predict.
However, doing that and passing, say, 0 for the y value results in wildly incorrect predictions, and changing the value affects predictions. Is there not a way to use OLS without comingling X and y?
libraryDependencies += "com.github.haifengl" % "smile-mkl" % "2.6.0"
CLARANS<double[]> clusters = PartitionClustering.run(20, () -> CLARANS.fit(x, new EuclideanDistance(), 6, 10));
PCA pca = PCA.fit(x);
pca.setProjection(2);
double[][] y = pca.project(x);
Canvas plot = ScatterPlot.of(y, clusters.y, '-').canvas();
Hi all. I'm having trouble getting started with basic linear algebra operations in Smile. I wonder if someone could help? In particular, I don't think I understand how symmetric matrices work.
val m2 = matrix(c(3.0,1.0),c(1.0,2.0)) // create a matrix, which is symmetric
m2.isSymmetric // returns false
m2.cholesky() // fails
If I create a symmetric matrix, isSymmetric
nevertheless returns false, so naturally, cholesky
fails. Is there something I need to do to tell Smile that the matrix is symmetric? Thanks,
val mat = matrix(c(3.0,3.5),c(2.0,2.0),c(0.0,1.0))
mat.qr().Q
returns a matrix with columns that are not orthonormal.