removed unnecessary code and dependencies

src/main/scala/models/mixture/GradientGaussianMixture.scala

@@ -3,7 +3,7 @@ package com.github.gradientgmm.models
 import com.github.gradientgmm.components.{UpdatableGaussianComponent, UpdatableWeights, Utils}
 import com.github.gradientgmm.optim.algorithms.{Optimizable, Optimizer, GradientAscent}
 
-import breeze.linalg.{diag, eigSym, DenseMatrix => BDM, DenseVector => BDV, Vector => BV, trace, sum}
+import breeze.linalg.{diag, DenseMatrix => BDM, DenseVector => BDV, Vector => BV, trace, sum}
 import breeze.numerics.sqrt
 
 import org.apache.spark.rdd.RDD
@@ -215,7 +215,7 @@ class GradientGaussianMixture private[models] (
   * Create a StreamingGaussianMixture object using the model's current state 
  
   */
-  def streamingModel: StreamingGaussianMixture = {
+  def asStreamingModel: StreamingGaussianMixture = {
     StreamingGaussianMixture(weights.weights,gaussians,optim)
   }
 
@@ -299,7 +299,7 @@ object GradientGaussianMixture{
 
     val sc = data.sparkContext
     val d = data.take(1)(0).size //get data dimensionality
-    val n = math.min(dataSize,pointsPerCl*k).toInt
+    val n = math.min(dataSize,pointsPerCl*k).toInt //in case the data has too few points
     var samples = sc.parallelize(data.takeSample(withReplacement = false, n, seed))
 
     //create kmeans model

src/main/scala/models/mixture/StreamingGaussianMixture.scala

@@ -3,17 +3,12 @@ package com.github.gradientgmm.models
 import com.github.gradientgmm.components.{UpdatableGaussianComponent, UpdatableWeights, Utils}
 import com.github.gradientgmm.optim.algorithms.{Optimizable, Optimizer, GradientAscent}
 
-import breeze.linalg.{diag, eigSym, DenseMatrix => BDM, DenseVector => BDV, Vector => BV, trace, sum}
-import breeze.numerics.sqrt
 
-import org.apache.spark.mllib.linalg.{Matrix => SM, Vector => SV, Vectors => SVS, Matrices => SMS}
-import org.apache.spark.mllib.clustering.{KMeans, KMeansModel}
+import org.apache.spark.mllib.linalg.{Vector => SV}
 import org.apache.spark.streaming.dstream.DStream
 import org.apache.spark.rdd.RDD
 
 
-import org.apache.log4j.Logger
-
 /**
   * Gradient-based Gaussian Mixture model for streaming data
   * See ''Hosseini, Reshad & Sra, Suvrit. (2017). An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization''
@@ -34,9 +29,9 @@ class StreamingGaussianMixture private[models] (
  
   */
 	def step(data: DStream[SV]) {
-	    data.foreachRDD { (rdd, time) =>
-	      step(rdd)
-	    }
+    data.foreachRDD { (rdd, time) =>
+      step(rdd)
+    }
 	}
 
 /**
@@ -49,7 +44,7 @@ class StreamingGaussianMixture private[models] (
     data.foreachRDD { (rdd, time) =>
       predict(rdd)
     }
-   }
+  }
 
 /**
   * Soft cluster membership prediction for streaming data
@@ -69,10 +64,9 @@ class StreamingGaussianMixture private[models] (
 
 object StreamingGaussianMixture{
 /**
-  * Creates a new StreamingGaussianMixture instance
+  * Creates a new StreamingGaussianMixture instance with a GradientAscent optimizer
   * @param weights Array of weights
   * @param gaussians Array of mixture components
-  * @param optim Optimizer object
  
   */
   def apply(
@@ -103,123 +97,4 @@ object StreamingGaussianMixture{
       optim)
   }
 
-/**
-  * Creates a new StreamingGaussianMixture instance initialized with the
-  * results of a K-means model fitted with a sample of the data
-  * @param data training data in the form of an RDD of Spark vectors
-  * @param optim Optimizer object
-  * @param k Number of components in the mixture
-  * @param nSamples Number of data points to train the K-means model
-  * @param nIters Number of iterations allowed for the K-means model
-  * @param seed random seed
-  */
-  def initialize(
-    data: RDD[SV],
-    optim: Optimizer,
-    k: Int,
-    nSamples: Int,
-    nIters: Int,
-    seed: Long = 0): StreamingGaussianMixture = {
-
-    val sc = data.sparkContext
-    val d = data.take(1)(0).size
-    val n = math.max(nSamples,2*k)
-    var samples = sc.parallelize(data.takeSample(withReplacement = false, n, seed))
-
-    //create kmeans model
-    val kmeansModel = new KMeans()
-      .setMaxIterations(nIters)
-      .setK(k)
-      .setSeed(seed)
-      .run(samples)
-
-    val means = kmeansModel.clusterCenters.map{case v => Utils.toBDV(v.toArray)}
-
-    //add means to sample points to avoid having cluster with zero points 
-    samples = samples.union(sc.parallelize(means.map{case v => SVS.dense(v.toArray)}))
-
-    // broadcast values to compute sample covariance matrices
-    val kmm = sc.broadcast(kmeansModel)
-    val scMeans = sc.broadcast(means)
-
-    // get empirical cluster proportions to initialize the mixture/s weights
-    //add 1 to counts to avoid division by zero
-    val proportions = samples
-      .map{case s => (kmm.value.predict(s),1)}
-      .reduceByKey(_ + _)
-      .sortByKey()
-      .collect()
-      .map{case (k,p) => p.toDouble}
-
-    val scProportions = sc.broadcast(proportions)
-
-    //get empirical covariance matrices
-    //also add a rescaled identity matrix to avoid starting with singular matrices 
-    val pseudoCov = samples
-      .map{case v => {
-          val prediction = kmm.value.predict(v)
-          val denom = math.sqrt(scProportions.value(prediction))
-          (prediction,(Utils.toBDV(v.toArray)-scMeans.value(prediction))/denom) }} // x => (x-mean)
-      .map{case (k,v) => (k,v*v.t)}
-      .reduceByKey(_ + _)
-      .map{case (k,v) => {
-        val avgVariance = math.max(1e-4,trace(v))/d
-        (k,v + BDM.eye[Double](d) * avgVariance)
-        }}
-      .sortByKey()
-      .collect()
-      .map{case (k,m) => m}
-
-    new StreamingGaussianMixture(
-      new UpdatableWeights(proportions.map{case p => p/(n+k)}), 
-      (0 to k-1).map{case i => UpdatableGaussianComponent(means(i),pseudoCov(i))}.toArray,
-      optim)
-
-  }
-
-  /**
-  * Fit a Gaussian Mixture Model (see [[https://en.wikipedia.org/wiki/Mixture_model#Gaussian_mixture_model]]).
-  * The model is initialized using a K-means algorithm over a small sample and then 
-  * fitting the resulting parameters to the data using this {GMMOptimization} object
-  * @param data Data to fit the model
-  * @param optim Optimization algorithm
-  * @param k Number of mixture components (clusters)
-  * @param batchSize number of samples processed per iteration
-  * @param maxIter maximum number of gradient ascent steps allowed
-  * @param convTol log-likelihood change tolerance for stopping criteria
-  * @param startingSampleSize Sample size for the K-means algorithm
-  * @param kMeansIters Number of iterations allowed for the K-means algorithm
-  * @param seed Random seed
-  * @return Fitted model
-  */
-  def fit(
-    data: RDD[SV], 
-    optim: Optimizer = new GradientAscent(), 
-    k: Int = 2,
-    batchSize: Option[Int] = None,
-    maxIter: Int = 100,
-    convTol: Double = 1e-6, 
-    startingSampleSize: Int = 50,
-    kMeansIters: Int = 20, 
-    seed: Int = 0): StreamingGaussianMixture = {
-
-    val model = initialize(
-                  data,
-                  optim,
-                  k,
-                  startingSampleSize,
-                  kMeansIters,
-                  seed)
-
-    if(batchSize.isDefined){
-      model.setBatchSize(batchSize.get)
-    }
-
-    model
-    .setMaxIter(maxIter)
-    .setConvergenceTol(convTol)
-    .step(data)
-
-    model
-  }
 }

src/main/scala/models/mixture/UpdatableGaussianMixture.scala

@@ -2,7 +2,7 @@ package com.github.gradientgmm.models
 
 import com.github.gradientgmm.components.{UpdatableGaussianComponent, UpdatableWeights, Utils}
 
-import breeze.linalg.{diag, eigSym, max, DenseMatrix => BDM, DenseVector => BDV, Vector => BV}
+import breeze.linalg.{diag, max, DenseMatrix => BDM, DenseVector => BDV, Vector => BV}
 
 import org.apache.spark.SparkContext
 import org.apache.spark.api.java.JavaRDD

src/main/scala/optim/algorithms/structure/Optimizable.scala

@@ -1,6 +1,6 @@
 package com.github.gradientgmm.optim.algorithms
 
-import breeze.linalg.{diag, eigSym, DenseMatrix => BDM, DenseVector => BDV, Vector => BV, trace, sum}
+import breeze.linalg.{diag, DenseMatrix => BDM, DenseVector => BDV, Vector => BV, trace, sum}
 import breeze.numerics.sqrt
 
 import org.apache.spark.mllib.linalg.{Vector => SV}