Revise readme

Author: Cheng-Lin-Li

.project

@@ -0,0 +1,18 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<projectDescription>
+	<name>Spark</name>
+	<comment></comment>
+	<projects>
+		<project>DataMining</project>
+	</projects>
+	<buildSpec>
+		<buildCommand>
+			<name>org.python.pydev.PyDevBuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+	</buildSpec>
+	<natures>
+		<nature>org.python.pydev.pythonNature</nature>
+	</natures>
+</projectDescription>

.pydevproject

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<?eclipse-pydev version="1.0"?><pydev_project>
+<pydev_pathproperty name="org.python.pydev.PROJECT_SOURCE_PATH">
+<path>/${PROJECT_DIR_NAME}</path>
+</pydev_pathproperty>
+<pydev_property name="org.python.pydev.PYTHON_PROJECT_VERSION">python 2.7</pydev_property>
+<pydev_property name="org.python.pydev.PYTHON_PROJECT_INTERPRETER">python 2.7.13</pydev_property>
+</pydev_project>

ALS/README.md

@@ -1 +1,42 @@
-#Spark
+## This is an implementation of Alternating Least Squares (ALS) algorithm in Spark with Python 2.7
+
+## Algorithm: Alternating Least Squares (ALS) Algorithm
+
+## Task:
+The task is to modify the parallel implementation of ALS (alternating least squares) algorithm in Spark, so that it takes a utility matrix as the input, and output the root-mean-square deviation (RMSE) into standard output or a file after each iteration. The code for the algorithm is als.py under the <spark-2.1.0 installation directory>/examples/src/main/python.
+
+#### Usage: bin/spark-submit ALS.py input-matrix n m f k p [output-file]
+  1. n is the number of rows (users) of the matrix
+  
+  
+  2. m is the number of columns (products). 
+  
+   
+  3. f is the number of dimensions/factors in the factor model. That is, U is n-by-f matrix, while V is f-by-m matrix.
+  
+  
+4. k is the number of iterations.
+
+
+  5. p, which is the number of partitions for the input-matrix
+
+  6. output-file, which is the path to the output file. This parameter is optional.
+  
+
+#### Input: Take a utility matrix (mat.dat) as the input
+
+#### Output: Output root-mean-square deviation (RMSE) into standard output or a file after each iteration
+After each iteration, output RMSE with 4 floating points.
+The "%.4f" % RMSE is adapted to format the RMSE value, and save into file as follows. 
+
+1.0019 
+
+
+0.9794 
+
+
+0.8464 
+
+
+…
+