请基于Spark ML库完成MNIST手写数字识别任务。 训练数据集与测试数据集分别存放在本地文件目录的如下位置： 训练数据集：/data/mnist_train.libsvm 测试数据集：/data/mnist_test.libsvm 要求：使用Spark SQL的方式计算识别精度。

好的，首先需要对数据集进行加载和预处理。我们可以使用Spark的ML库中的`LibSVMVectorReader`读取数据集文件，并将其转换为DataFrame格式。

from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import IndexToString
from pyspark.ml.feature import StandardScaler
from pyspark.ml import Pipeline

# 读取训练数据集和测试数据集
train_data = spark.read.format("libsvm").load("/data/mnist_train.libsvm")
test_data = spark.read.format("libsvm").load("/data/mnist_test.libsvm")

# 对数据进行预处理
assembler = VectorAssembler(inputCols=train_data.columns[1:], outputCol="features")
scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures", withStd=True, withMean=False)
labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(train_data)
labelConverter = IndexToString(inputCol="prediction", outputCol="predictedLabel", labels=labelIndexer.labels)

train_data = assembler.transform(train_data)
test_data = assembler.transform(test_data)

scalerModel = scaler.fit(train_data)
train_data = scalerModel.transform(train_data)
test_data = scalerModel.transform(test_data)

接着，我们可以定义一个多层感知机分类器，并使用训练数据集对其进行训练。

# 定义多层感知机分类器
layers = [784, 128, 64, 10]
trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=128, seed=1234)

# 训练模型
model = trainer.fit(train_data)

最后，我们可以使用测试数据集对模型进行评估，并计算识别精度。

# 对测试数据集进行预测
predictions = model.transform(test_data)

# 计算识别精度
evaluator = MulticlassClassificationEvaluator(predictionCol="prediction", labelCol="indexedLabel", metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g" % (1.0 - accuracy))

完整代码如下：

from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import IndexToString
from pyspark.ml.feature import StandardScaler
from pyspark.ml import Pipeline

# 读取训练数据集和测试数据集
train_data = spark.read.format("libsvm").load("/data/mnist_train.libsvm")
test_data = spark.read.format("libsvm").load("/data/mnist_test.libsvm")

# 对数据进行预处理
assembler = VectorAssembler(inputCols=train_data.columns[1:], outputCol="features")
scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures", withStd=True, withMean=False)
labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(train_data)
labelConverter = IndexToString(inputCol="prediction", outputCol="predictedLabel", labels=labelIndexer.labels)

train_data = assembler.transform(train_data)
test_data = assembler.transform(test_data)

scalerModel = scaler.fit(train_data)
train_data = scalerModel.transform(train_data)
test_data = scalerModel.transform(test_data)

# 定义多层感知机分类器
layers = [784, 128, 64, 10]
trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=128, seed=1234)

# 训练模型
model = trainer.fit(train_data)

# 对测试数据集进行预测
predictions = model.transform(test_data)

# 计算识别精度
evaluator = MulticlassClassificationEvaluator(predictionCol="prediction", labelCol="indexedLabel", metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g" % (1.0 - accuracy))