程序代写案例-PS2 1400
时间:2021-04-05
Q1. (40 marks)
Consider the following base cuboid Sales with four tuples and the aggregate function
SUM:
Location T ime Item Quantity
Sydney 2005 PS2 1400
Sydney 2006 PS2 1500
Sydney 2006 Wii 500
Melbourne 2005 XBox 360 1700
Location, Time, and Item are dimensions and Quantity is the measure. Suppose the
system has built-in support for the value ALL.
(1) List the tuples in the complete data cube of R in a tabular form with 4 attributes,
i.e., Location, T ime, Item,SUM(Quantity)?
(2) Write down an equivalent SQL statement that computes the same result (i.e., the
cube). You can only use standard SQL constructs, i.e., no CUBE BY clause.
(3) Consider the following ice-berg cube query:
SELECT Location, Time, Item, SUM(Quantity)
FROM Sales
CUBE BY Location, Time, Item
HAVING COUNT(*) > 1
Draw the result of the query in a tabular form.
(4) Assume that we adopt a MOLAP architecture to store the full data cube of R, with
the following mapping functions:
fLocation(x) =

1 if x = ‘Sydney’,
2 if x = ‘Melbourne’,
0 if x = ALL.
fT ime(x) =

1 if x = 2005,
2 if x = 2006,
0 if x = ALL.
1
2fItem(x) =

1 if x = ‘PS2’,
2 if x = ‘XBox 360’,
3 if x = ‘Wii’,
0 if x = ALL.
If we want to draw the MOLAP cube (i.e., sparse multi-dimensional array) in
a tabular form of (ArrayIndex, V alue), then which of the following function is
feasible? Why? You also need to draw the MOLAP cube.
• f(x) = 9 · fLocation(x) + 3 · fT ime(x) + fItem(x)
• f(x) = 16 · fLocation(x) + 4 · fT ime(x) + fItem(x)
Q2. (30 marks)
Consider the following training examples which are used to construct a decision tree to
help predict whether a patient is likely to have a lung cancer.
Patient ID Gender Smokes? Chest pain? Cough? Lung Cancer
1 Female Yes Yes Yes Yes
2 Male Yes No Yes Yes
3 Male No No No Yes
4 Female No Yes Yes No
5 Male Yes Yes No Yes
6 Male No Yes Yes No
(1) Use Gini index to construct a decision tree that predicts whether a patient is likely
to have a lung cancer. You need to show every step of the construction.
(2) Translate your decision tree into decision rules.
Q3. (30 marks)
Consider binary classification where the class attribute y takes two values: 0 or 1.
Let the feature vector for a test instance to be a d-dimention column vector x. A linear
classifier with the model paramter w (which is a d-dimension column vector) is the following
function:
y =
{
1 , if wTx > 0
0 , otherwise.
We make additional simplifying assumptions: x is a binary vector (i.e., each dimension
of x take only two values: 0 or 1).
(1) Prove that if the feature vectors are d-dimension, then a Na¨ıve Bayes classifier is
a linear classifier in a d + 1-dimension space. You need to explicitly write out the
vector w that the Na¨ıve Bayes classifier learns.
3(2) It is obvious that the Logistic Regression classifier learned on the same training
dataset as the Na¨ıve Bayes is also a linear classifier in the same d + 1-dimension
space. Let the parameter w learned by the two classifiers be wLR and wNB,
respectively. Briefly explain why learning wNB is much easier than learning wLR.
Hint1.log

i
x
i
=

i
logx
i
Submission
Please write down your answers in a file named ass1.pdf.























































































学霸联盟


essay、essay代写
essay、essay代写