Python linalg._convert_to_vector函数代码示例

 def appendBias(data):
     """
     Returns a new vector with `1.0` (bias) appended to
     the end of the input vector.
     """
     vec = _convert_to_vector(data)
     if isinstance(vec, SparseVector):
         newIndices = np.append(vec.indices, len(vec))
         newValues = np.append(vec.values, 1.0)
         return SparseVector(len(vec) + 1, newIndices, newValues)
     else:
         return _convert_to_vector(np.append(vec.toArray(), 1.0))

def _regression_train_wrapper(train_func, modelClass, data, initial_weights):
    from pyspark.mllib.classification import LogisticRegressionModel
    first = data.first()
    if not isinstance(first, LabeledPoint):
        raise TypeError("data should be an RDD of LabeledPoint, but got %s" % type(first))
    if initial_weights is None:
        initial_weights = [0.0] * len(data.first().features)
    if (modelClass == LogisticRegressionModel):
        weights, intercept, numFeatures, numClasses = train_func(
            data, _convert_to_vector(initial_weights))
        return modelClass(weights, intercept, numFeatures, numClasses)
    else:
        weights, intercept = train_func(data, _convert_to_vector(initial_weights))
        return modelClass(weights, intercept)

    def update(self, data, decayFactor, timeUnit):
        """Update the centroids, according to data

        :param data:
          RDD with new data for the model update.
        :param decayFactor:
          Forgetfulness of the previous centroids.
        :param timeUnit:
          Can be "batches" or "points". If points, then the decay factor
          is raised to the power of number of new points and if batches,
          then decay factor will be used as is.
        """
        if not isinstance(data, RDD):
            raise TypeError("Data should be of an RDD, got %s." % type(data))
        data = data.map(_convert_to_vector)
        decayFactor = float(decayFactor)
        if timeUnit not in ["batches", "points"]:
            raise ValueError(
                "timeUnit should be 'batches' or 'points', got %s." % timeUnit)
        vectorCenters = [_convert_to_vector(center) for center in self.centers]
        updatedModel = callMLlibFunc(
            "updateStreamingKMeansModel", vectorCenters, self._clusterWeights,
            data, decayFactor, timeUnit)
        self.centers = array(updatedModel[0])
        self._clusterWeights = list(updatedModel[1])
        return self

def _regression_train_wrapper(train_func, modelClass, data, initial_weights):
    first = data.first()
    if not isinstance(first, LabeledPoint):
        raise ValueError("data should be an RDD of LabeledPoint, but got %s" % first)
    initial_weights = initial_weights or [0.0] * len(data.first().features)
    weights, intercept = train_func(data, _convert_to_vector(initial_weights))
    return modelClass(weights, intercept)

    def predict(self, x):
        """
        Predict the label of one or more examples.

        :param x:  Data point (feature vector),
                   or an RDD of data points (feature vectors).
        """
        SerDe = self._sc._jvm.SerDe
        ser = PickleSerializer()
        if isinstance(x, RDD):
            # Bulk prediction
            first = x.take(1)
            if not first:
                return self._sc.parallelize([])
            if not isinstance(first[0], Vector):
                x = x.map(_convert_to_vector)
            jPred = self._java_model.predict(x._to_java_object_rdd()).toJavaRDD()
            jpyrdd = self._sc._jvm.PythonRDD.javaToPython(jPred)
            return RDD(jpyrdd, self._sc, BatchedSerializer(ser, 1024))

        else:
            # Assume x is a single data point.
            bytes = bytearray(ser.dumps(_convert_to_vector(x)))
            vec = self._sc._jvm.SerDe.loads(bytes)
            return self._java_model.predict(vec)

    def predictSoft(self, x):
        """
        Find the membership of point 'x' or each point in RDD 'x' to all mixture components.

        :param x:    vector or RDD of vector represents data points.
        :return:     the membership value to all mixture components for vector 'x'
                     or each vector in RDD 'x'.
        """
        if isinstance(x, RDD):
            means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians])
            membership_matrix = callMLlibFunc(
                "predictSoftGMM", x.map(_convert_to_vector), _convert_to_vector(self.weights), means, sigmas
            )
            return membership_matrix.map(lambda x: pyarray.array("d", x))
        else:
            return self.call("predictSoft", _convert_to_vector(x)).toArray()

 def predict(self, x):
     """
     Predict the value of the dependent variable given a vector x
     containing values for the independent variables.
     """
     x = _convert_to_vector(x)
     return self.weights.dot(x) + self.intercept

 def train(
     cls,
     rdd,
     k,
     maxIterations=100,
     runs=1,
     initializationMode="k-means||",
     seed=None,
     initializationSteps=5,
     epsilon=1e-4,
     initialModel=None,
 ):
     """Train a k-means clustering model."""
     clusterInitialModel = []
     if initialModel is not None:
         if not isinstance(initialModel, KMeansModel):
             raise Exception(
                 "initialModel is of " + str(type(initialModel)) + ". It needs " "to be of <type 'KMeansModel'>"
             )
         clusterInitialModel = [_convert_to_vector(c) for c in initialModel.clusterCenters]
     model = callMLlibFunc(
         "trainKMeansModel",
         rdd.map(_convert_to_vector),
         k,
         maxIterations,
         runs,
         initializationMode,
         seed,
         initializationSteps,
         epsilon,
         clusterInitialModel,
     )
     centers = callJavaFunc(rdd.context, model.clusterCenters)
     return KMeansModel([c.toArray() for c in centers])

 def save(self, sc, path):
     """
     Save this model to the given path.
     """
     java_centers = _py2java(sc, [_convert_to_vector(c) for c in self.centers])
     java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel(java_centers)
     java_model.save(sc._jsc.sc(), path)

 def computeCost(self, rdd):
     """
     Return the K-means cost (sum of squared distances of points to
     their nearest center) for this model on the given data.
     """
     cost = callMLlibFunc("computeCostKmeansModel", rdd.map(_convert_to_vector),
                          [_convert_to_vector(c) for c in self.centers])
     return cost

    def test_serialize(self):
        from scipy.sparse import lil_matrix
        lil = lil_matrix((4, 1))
        lil[1, 0] = 1
        lil[3, 0] = 2
        sv = SparseVector(4, {1: 1, 3: 2})
        self.assertEqual(sv, _convert_to_vector(lil))
        self.assertEqual(sv, _convert_to_vector(lil.tocsc()))
        self.assertEqual(sv, _convert_to_vector(lil.tocoo()))
        self.assertEqual(sv, _convert_to_vector(lil.tocsr()))
        self.assertEqual(sv, _convert_to_vector(lil.todok()))

        def serialize(l):
            return ser.loads(ser.dumps(_convert_to_vector(l)))
        self.assertEqual(sv, serialize(lil))
        self.assertEqual(sv, serialize(lil.tocsc()))
        self.assertEqual(sv, serialize(lil.tocsr()))
        self.assertEqual(sv, serialize(lil.todok()))

 def predict(self, x):
     """
     Predict the value of the dependent variable given a vector or
     an RDD of vectors containing values for the independent variables.
     """
     if isinstance(x, RDD):
         return x.map(self.predict)
     x = _convert_to_vector(x)
     return self.weights.dot(x) + self.intercept

    def setInitialWeights(self, initialWeights):
        """
        Set the initial value of weights.

        This must be set before running trainOn and predictOn
        """
        initialWeights = _convert_to_vector(initialWeights)
        self._model = LinearRegressionModel(initialWeights, 0)
        return self

 def predict(self, x):
     """
     Return the most likely class for a data vector
     or an RDD of vectors
     """
     if isinstance(x, RDD):
         return x.map(lambda v: self.predict(v))
     x = _convert_to_vector(x)
     return self.labels[numpy.argmax(self.pi + x.dot(self.theta.transpose()))]

 def predict_all(self, x):
     if isinstance(x, RDD):
         return x.map(lambda v: self.predict_all(v))
     x = _convert_to_vector(x)
     log_probs = self.pi + x.dot(self.theta.transpose())
     scaled_log_probs = scale(log_probs)
     int_lables = [int(l_i) for l_i in self.labels]
     labels_and_log_probs = zip(int_lables, scaled_log_probs)
     return sorted(labels_and_log_probs, key=lambda x: x[1], reverse=True)