ITI 1121程序讲解、辅导java程序、java编程设计调试 辅导留学生Prolog|讲解R语言编程

日期：2021-02-07 10:59

ITI 1121. Introduction to Computing II
Winter 2021
Assignment 1
(Last modified on January 13, 2021)
Deadline: February 5, 2021, 11:30 pm
Learning objectives
? Edit, compile and run Java programs
? Utilize arrays to store information
? Apply basic object-oriented programming concepts
? Understand the university policies for academic integrity
Introduction
This year, we are going to implement, through a succession of assignments, a simplified version of a useful machine
learning technique, called decision tree classification. If you are not familiar with decision trees and are curious to
know what they are, you may wish to have a quick look at the following Wikipedia page: https://en.wikipedia.
org/wiki/Decision_tree_learning.
For Assignment 1, however, you are not going to do anything that is specific to decision trees; you can complete
Assignment 1 without any knowledge of decision trees! We will get to decision trees only in Assignments 2 and 3.
If you find the above Wikipedia page overwhelming, fear not! As we go along, we will provide you with simple
and accessible material to read on decision tree classification. Ultimately, the version of decision tree classification
that you implement, despite still being extremely useful, has many of the complexities of the more advanced
implementations removed (for example, handling “unknown” values in your training data).
As far as the current assignment – Assignment 1 – is concerned, we have modest goals: we would like to read an
input file, which will (in future assignments) constitute the training data for our learning algorithm, and perform
some basic tasks that are prerequisites to virtually any type of machine learning.
Specifically, you will be implementing the following tasks in Assignment 1:
? Task 1. Parsing comma-separated values (CSV) from a given data file and populating appropriate data structures
in memory
? Task 2. Extracting certain summary data (metadata) about the characteristics of the input data; this metadata
will come handy for the construction of decision trees in future assignments.
These two tasks are best illustrated with a simple example. Suppose we have a CSV file named weather.csv
with the content shown in Figure 1.
1 The data is simply a table. The first (non-empty) row in the file provides
the names of the table columns in a comma-separated format. Each column represents an attribute (also called a
feature). The remaining (non-empty) rows are the datapoints.
In our example, each datapoint is a historical observation about weather conditions (in terms of outlook, temperature
in fahrenheit, humidity and wind), and whether it has been possible to “play” a certain tournament (for
example, cricket) outside. What a machine learning algorithm can do here is to “learn from examples” and help
decide / predict whether one can play a tournament on a given day according to the weather conditions on that day.
Now, going backing to Task 1 and Task 2, below is what each of these tasks would do with the data in Figure 1.
1This example is borrowed from “Data Mining: Practical Machine Learning Tools and Techniques” 3rd Ed. (2011) by Ian H. Witten, Eibe
Frank and Mark A. Hall.
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outlook,temperature,humidity,windy,play
sunny,85,85,FALSE,no
sunny,80,90,TRUE,no
overcast,83,86,FALSE,yes
rainy,70,96,FALSE,yes
rainy,68,80,FALSE,yes
rainy,65,70,TRUE,no
overcast,64,65,TRUE,yes
sunny,72,95,FALSE,no
sunny,69,70,FALSE,yes
rainy,75,80,FALSE,yes
sunny,75,70,TRUE,yes
overcast,72,90,TRUE,yes
overcast,81,75,FALSE,yes
rainy,71,91,TRUE,no
Figure 1: Example CSV file (weather.csv)
String[5]
String[14][5]
outlook temperature humidity windy play
sunny 85 85 FALSE no
sunny 80 90 TRUE no
overcast 83 86 FALSE yes
rainy 70 96 FALSE yes
rainy 68 80 FALSE yes
rainy 65 70 TRUE no
overcast 64 65 TRUE yes
sunny 72 95 FALSE no
sunny 69 70 FALSE yes
rainy 75 80 FALSE yes
sunny 75 70 TRUE yes
overcast 72 90 TRUE yes
overcast 81 75 FALSE yes
rainy 71 91 TRUE no
attributeNames
matrix
Figure 2: Results of parsing our example input file
? Task 1 will parse the input data and build the conceptual memory representation shown in Figure 2. More
precisely, we get (1) an instance variable, attributeNames (discussed later), instantiated with a String array
of length 5 and containing the column names, and (2) an instance variable, matrix (also discussed later),
instantiated with a two-dimensional String array (of size 14×5) and populated with the datapoints in the file.
? Task 2 will identify the unique values observed in each column. If all the values in a column happen to be
numeric, then the column is found to be of numeric type. Otherwise, the column will be of nominal type,
meaning that the values in the column are to be treated as labels without any quantitative value associated
with them. For our example file, the column types and the set of unique values for each column would be as
shown in Figure 3. Note that, for this assignment, you do not need to sort the numeric value sets in either
ascending or descending order. This becomes necessary only in the future assignments.
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1) outlook (nominal): [’sunny’, ’overcast’, ’rainy’]
2) temperature (numeric): [85, 80, 83, 70, 68, 65, 64, 72, 69, 75, 81, 71]
3) humidity (numeric): [85, 90, 86, 96, 80, 70, 65, 95, 75, 91]
4) windy (nominal): [’FALSE’, ’TRUE’]
5) play (nominal): [’no’, ’yes’]
Figure 3: Derived column types and unique values sets for our example input file
outlook,temperature,humidity,windy,play
sunny, 85, 85,FALSE,no
sunny,80,90,TRUE, no
overcast,83,86,FALSE, yes
rainy,70,96, FALSE,yes
rainy,68,80,FALSE, yes
rainy,65,70,TRUE,no
overcast,64,65, TRUE,yes
sunny,72,95,FALSE,no
sunny, 69,70,FALSE,yes
rainy,75, 80,FALSE,yes
sunny,75,70, TRUE,yes
overcast,72,90, TRUE,yes
overcast,81,75,FALSE,yes
rainy, 71,91,TRUE,no
Figure 4: Example with surrounding spaces and empty lines that need to be ignored
Important Considerations (Read Carefully!)
While the assignment is conceptually simple, there are some important consideration that you need to carefully
pay attention to in your implementation.
Determining the size of the arrays to instantiate: You will be storing the attribute names and datapoints using
two instance variables that are respectively declared as follows:
private String[] attributeNames;
private String[][] matrix;
One problem that you have to deal with is how to instantiate these variables. To do so, you need to know the
number of attributes (columns) and the number of datapoints. You can know the former number only after
counting the attributes names on the first (non-empty) line of the file. As for the latter (number of datapoints),
you can only know this once you have traversed the entire file. Later on in the course, we will see “expandible”
data structure like linked lists, which do not have a fixed size, allowing elements to be added to them as you
go along. For this assignment, you are expressly forbidden from using lists or similar data structures with
non-fixed sizes. Instead, you are expected to work with fixed-size arrays. For this assignment, the easiest way
to instantiate the arrays is through a two-pass strategy. This means that you will go over the input file twice.
In the first pass, you merely count the number of columns and datapoints. With these numbers known, you
can instantiate attributeNames and matrix. Then, in a second pass, you can populate (the now-instantiated)
attributeNames and matrix. Note that, as illustrated in Figure 2, you are expected to instantiate matrix as
a row × column array, as oppposed to a column × row array. While this latter strategy is correct too, you are
asked to use the former (that is, row × column) as a convention throughout this assignment.
Removing blank spaces and empty lines: The blank spaces surrounding attribute names and values should be
discarded. For example, consider the input file in Figure 4. This file is the same as the one in Figure 1, only
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outlook,’temperature, in fahrenheit’,humidity,windy,play
sunny,85,85,FALSE,no
sunny,80,90,TRUE,no
overcast,83,86,FALSE,yes
’rainy, mild’,70,96,FALSE,yes
’rainy, mild’,68,80,FALSE,yes
’rainy, heavy’,65,70,TRUE,no
overcast,64,65,TRUE,yes
sunny,72,95,FALSE,no
sunny,69,70,FALSE,yes
’rainy, mild’,75,80,FALSE,yes
sunny,75,70,TRUE,yes
overcast,72,90,TRUE,yes
overcast,81,75,FALSE,yes
’rainy, heavy’,71,91,TRUE,no
Figure 5: Example usage of commas in attribute names and values
1) outlook (nominal): [’sunny’, ’overcast’, ’rainy, mild’, ’rainy, heavy’]
2) temperature, in fahrenheit (numeric): [85, 80, 83, 70, 68, 65, 64, 72, 69, 75, 81, 71]
3) humidity (numeric): [85, 90, 86, 96, 80, 70, 65, 95, 75, 91]
4) windy (nominal): [’FALSE’, ’TRUE’]
5) play (nominal): [’no’, ’yes’]
Figure 6: Metadata derived from the input shown in Figure 5
with some surrounding spaces added. These surrounding spaces need to be trimmed and ignored. The same
goes with empty lines. Empty lines can be treated as non-existent.
Supporting commas in attribute names and values: Since commas are used as separators (delimiters), it is important
to provide a way to support commas within attribute names and attribute values. To do so, you will need
to implement an escape sequence mechanism. You will do so using single quotes (’). More precisely, commas
are to be treated as regular characters if a text segment is embraced with single quotes. To illustrate, consider
the example input in Figure 5. The metadata information derived from this input is shown in Figure 6. While
not explicitly shown by Figure 6, the values to store in attributeNames and matrix are obviously affected
when escape sequences with single quotes are present in the input file.
Missing attribute values: There may be situations where not all attribute values are known (for example, due to
incomplete data collection). In such cases, the attribute values in question may be left empty. Your implementation
needs to be able empty (missing) attribute values. You can choose to represent missing values with
a special value, for example ‘MISSING’. Alternatively, you can choose to represent missing values with an
empty string (‘’). To illustrate, consider the input file in Figure 7, where some values are missing. The metadata
derived from this input file is shown in Figure 8. Here, we have chosen to represent missing values with
the empty string. For this assignment, you should designate any column that has missing values as nominal.
For example, in Figure 7, some of the values for the humidity attribute are missing. This has resulted in
humidity to no longer be a numeric attribute but rather a nominal one, as shown in Figure 8.
Efficiency in identifying unique attribute values: You need to be prepared for the possibility that your input file
would be large. One particular place you need to be careful with is when you are determining the unique
set of values that a given attribute can assume. Your implementation should be efficient (hint: should not do
futile search) in order to avoid a quadratic runtime.
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outlook,temperature,humidity,windy,play
sunny,85,85,FALSE,
sunny,80,90,TRUE,no
overcast,83,,FALSE,
rainy,70,96,FALSE,yes
rainy,68,80,FALSE,yes
rainy,65,70,TRUE,no
overcast,64,65,TRUE,yes
sunny,72,,FALSE,no
sunny,69,70,FALSE,yes
rainy,75,80,FALSE,yes
sunny,75,70,TRUE,yes
overcast,72,90,TRUE,yes
overcast,81,75,FALSE,yes
rainy,71, ,TRUE,
Figure 7: Example input file with missing values
1) outlook (nominal): [’sunny’, ’overcast’, ’rainy’]
2) temperature (numeric): [85, 80, 83, 70, 68, 65, 64, 72, 69, 75, 81, 71]
3) humidity (nominal): [’85’, ’90’, ’’, ’96’, ’80’, ’70’, ’65’, ’75’]
4) windy (nominal): [’FALSE’, ’TRUE’]
5) play (nominal): [’’, ’no’, ’yes’]
Figure 8: Metadata derived from the input file shown in Figure 7
Implementation
We are now ready to program our solution. We will only need two classes for this. For the assignment, you need to
follow the patterns that we provide. You cannot change any of the signatures of the methods (that is you cannot
modify the methods at all). You cannot add new public methods or variables. You can, however, add new private
methods to improve the readability or the organization of your code.
DataSet
There are a number of methods in DataSet that you need to either complete or implement from scratch. Guidance
is provided in the template code in the form of comments. The locations where you need to write code have been
clearly indicated with an inline comment that reads as follows:
// WRITE YOUR CODE HERE!
The easiest way to navigate the template code is to start from the main method of the DataSet class, shown in
Figure 9. Intuitively, this method works as follows: First, it reads from the standard input the name of the CSV file
to process. Next, it creates an instance of DataSet; the constructor of DataSet will process the input file and populate
attributeNames and matrix (explained earlier). Finally, the main method prints to the standard output the
metadata of the instance of the DataSet that was just created. Doing so requires calling the metadataToString() instance
method. To better illustrate metadataToString(), Figure 10 shows the return value of metadataToString()
by our reference implementation for the input file of Figure 1 (the instructors’ reference implementation will be
released to you after the assignment due date).
Please note that there are a couple of technicalities which you will learn about only later in the course. One is
how to process files. The other (and much more important technicality, for that matter) is the notion of exceptions in
Java. For this assignment, the template code for file processing is provided wherever it is needed. As for exceptions,
you do not have to deal with them in this assignment, but you will see that some methods in the code are declared
as throwing exceptions. You can ignore these exception declarations for now.
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public static void main(String[] args) throws Exception {
System.out.print("Please enter the name of the CSV file to read: ");
Scanner scanner = new Scanner(System.in);
String strFilename = scanner.nextLine();
DataSet dataset = new DataSet(strFilename);
System.out.print(dataset.metadataToString());
}
Figure 9: The main method in DataSet
Number of attributes: 5
Number of datapoints: 14
* * * Attribute value sets * * *
1) outlook (nominal): [’sunny’, ’overcast’, ’rainy’]
2) temperature (numeric): [85, 80, 83, 70, 68, 65, 64, 72, 69, 75, 81, 71]
3) humidity (numeric): [85, 90, 86, 96, 80, 70, 65, 95, 75, 91]
4) windy (nominal): [’FALSE’, ’TRUE’]
5) play (nominal): [’no’, ’yes’]
Figure 10: String returned by metadataToString() for the input file of Figure 1
Util
The Util class is provided in order to faciliate the implementation of the metadataToString() method, which you
will be implementing in the DataSet class (see the template code). The Util class provides four static methods:
? public static boolean isNumeric(String str): Checks if str represents a numeric value
? isArrayNumeric(String[] array): Checks an array of strings, array, and returns true if and only if array
is non-empty and all its elements represent numeric values
? public static String nominalArrayToString(String[] array): Produces a string representation of an
array of nominals. Note that all nominal labels are embraced with single quotes.
? public static String numericArrayToString(String[] array): Produces a string representation of an
array of numerics. Unlike nominals, numeric values are not embraced with single quotes in the representation.
JUnit Tests
We are providing a set of JUnit tests for the class DataSet. These tests should help make sure that your implementation
is correct. They can further help clarify the expected behaviour of this class, if need be. Please note that the
DataSetTest class assumes that the following CSV files are located in the current directly: credit-info-with-commas.csv,
weather-nominal.csv, credit-info.csv, weather-numeric.csv, large.csv, weather-with-spaces.csv, and missing-values.csv.
You can find all these CSV files in the datasets directory of the template zip file you have been provided with.
Academic Integrity
This part of the assignment is meant to raise awareness concerning plagiarism and academic integrity. Please read
the following documents.
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? https://www.uottawa.ca/administration-and-governance/academic-regulation-14-other-important-information
? https://www.uottawa.ca/vice-president-academic/academic-integrity
Cases of plagiarism will be dealt with according to the university regulations. By submitting this assignment, you
acknowledge:
1. I have read the academic regulations regarding academic fraud.
2. I understand the consequences of plagiarism.
3. With the exception of the source code provided by the instructors for this course, all the source code is mine.
4. I did not collaborate with any other person, with the exception of my partner in the case of team work.
? If you did collaborate with others or obtained source code from the Web, then please list the names of
your collaborators or the source of the information, as well as the nature of the collaboration. Put this
information in the submitted README.txt file. Marks will be deducted proportional to the level of help
provided (from 0 to 100%).
Rules and regulation
? Follow all the directives available on the assignment directives web page.
? Submit your assignment through the on-line submission system virtual campus.
? You must preferably do the assignment in teams of two, but you can also do the assignment individually.
? You must use the provided template classes below.
? If you do not follow the instructions, your program will make the automated tests fail and consequently your
assignment will not be graded.
? We will be using an automated tool to compare all the assignments against each other (this includes both, the
French and English sections). Submissions that are flagged by this tool will receive the grade of 0.
? It is your responsibility to make sure that BrightSpace has received your assignment. Late submissions will
not be graded.
Files
You must hand in a zip file (no other file format will be accepted). The name of the top directory has to have the
following form: a1_3000000_3000001, where 3000000 and 3000001 are the student numbers of the team members
submitting the assignment (simply repeat the same number if your team has one member). The name of the
folder starts with the letter “a” (lowercase), followed by the number of the assignment, here 1. The parts are
separated by the underscore (not the hyphen). There are no spaces in the name of the directory. The archive
a1_3000000_3000001.zip contains the files that you can use as a starting point. Your submission must contain the
following files.
? README.txt
– A text file that contains the names of the two partners for the assignments, their student ids, section, and
a short description of the assignment (one or two lines).
? DataSet.java
? Util.java2
? StudentInfo.java (Make sure to update the file, so that the display() method shows your personal information).
2You are not supposed to change Util.java; you simply resubmit the file given to you.