add more tests

mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala

@@ -746,37 +746,40 @@ class GeneralizedLinearRegressionSuite
   test("generalized linear regression: intercept only") {
     /*
       R code:
-      y <- c(17, 19, 23, 29)
+
+      library(statmod)
+      y <- c(1.0, 0.5, 0.7, 0.3)
       w <- c(1, 2, 3, 4)
-      model1 <- glm(y ~ 1, family = poisson)
-      model2 <- glm(y ~ 1, family = poisson, weights = w)
-      as.vector(c(coef(model1), coef(model2)))
-      for (fam in c("gaussian", "poisson", "Gamma")) {
+      for (fam in list(gaussian(), poisson(), binomial(), Gamma(), tweedie(1.6))) {
         model1 <- glm(y ~ 1, family = fam)
         model2 <- glm(y ~ 1, family = fam, weights = w)
         print(as.vector(c(coef(model1), coef(model2))))
       }
-      [1] 22 24
-      [1] 3.091042 3.178054
-      [1] 0.04545455 0.04166667
+      [1] 0.625 0.530
+      [1] -0.4700036 -0.6348783
+      [1] 0.5108256 0.1201443
+      [1] 1.600000 1.886792
+      [1] 1.325782 1.463641
      */
 
     val dataset = Seq(
-      Instance(17.0, 1.0, Vectors.zeros(0)),
-      Instance(19.0, 2.0, Vectors.zeros(0)),
-      Instance(23.0, 3.0, Vectors.zeros(0)),
-      Instance(29.0, 4.0, Vectors.zeros(0))
+      Instance(1.0, 1.0, Vectors.zeros(0)),
+      Instance(0.5, 2.0, Vectors.zeros(0)),
+      Instance(0.7, 3.0, Vectors.zeros(0)),
+      Instance(0.3, 4.0, Vectors.zeros(0))
     ).toDF()
 
-    val expected = Seq(22.0, 24.0, 3.0910, 3.1781, 0.0455, 0.0417)
+    val expected = Seq(0.625, 0.530, -0.4700036, -0.6348783, 0.5108256, 0.1201443,
+      1.600000, 1.886792, 1.325782, 1.463641)
 
     import GeneralizedLinearRegression._
 
     var idx = 0
-    for (family <- Seq("gaussian", "poisson", "gamma")) {
+    for (family <- Seq("gaussian", "poisson", "binomial", "gamma", "tweedie")) {
       for (useWeight <- Seq(false, true)) {
         val trainer = new GeneralizedLinearRegression().setFamily(family)
         if (useWeight) trainer.setWeightCol("weight")
+        if (family == "tweedie") trainer.setVariancePower(1.6)
         val model = trainer.fit(dataset)
         val actual = model.intercept
         assert(actual ~== expected(idx) absTol 1E-3, "Model mismatch: intercept only GLM with " +