Document 2: Contains only "trucks". Document 3: Contains both "cars" and "trucks". Document 4: Contains neither "cars" nor "trucks". To evaluate Search A, clearly the search "(cars OR trucks)" will hit on Documents 1, 2, ar earch (which is Search A) will hit everything else, which is Document 4. Evaluating Search B, the search "(NOT cars)" will hit on documents that do not contain and 4. Similarly the search "(NOT trucks)" will hit on Documents 1 and 4. Applying the A searches (which is Search B) will hit on the documents that are common to these two s Please write a Python program to De Morgan's Law via document search. You can use four text files as document 1, 2 3 and 4. Search A: NOT (cars OR trucks) et A list of all docs (a, b, c, d) et B list of docs with trucks (a, c) et C = list of docs with cars (b, c) et D = list of docs with cars or trucks (a, b, c) et E = not D (d)
Document 2: Contains only "trucks". Document 3: Contains both "cars" and "trucks". Document 4: Contains neither "cars" nor "trucks". To evaluate Search A, clearly the search "(cars OR trucks)" will hit on Documents 1, 2, ar earch (which is Search A) will hit everything else, which is Document 4. Evaluating Search B, the search "(NOT cars)" will hit on documents that do not contain and 4. Similarly the search "(NOT trucks)" will hit on Documents 1 and 4. Applying the A searches (which is Search B) will hit on the documents that are common to these two s Please write a Python program to De Morgan's Law via document search. You can use four text files as document 1, 2 3 and 4. Search A: NOT (cars OR trucks) et A list of all docs (a, b, c, d) et B list of docs with trucks (a, c) et C = list of docs with cars (b, c) et D = list of docs with cars or trucks (a, b, c) et E = not D (d)
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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![De Morgan’s laws commonly apply to text searching using Boolean operators AND, OR, and NOT. Consider a set of documents containing the words “cars” and “trucks”. De Morgan’s laws hold that these two searches will return the same set of documents:
**Search A:** NOT (cars OR trucks)
**Search B:** (NOT cars) AND (NOT trucks)
The corpus of documents containing “cars” or “trucks” can be represented by four documents:
1. Document 1: Contains only the word “cars”.
2. Document 2: Contains only “trucks”.
3. Document 3: Contains both “cars” and “trucks”.
4. Document 4: Contains neither “cars” nor “trucks”.
To evaluate Search A, clearly the search “(cars OR trucks)” will hit on Documents 1, 2, and 3. So the negation of that search (which is Search A) will hit everything else, which is Document 4.
Evaluating Search B, the search “(NOT cars)” will hit on documents that do not contain “cars”, which is Documents 2 and 4. Similarly the search “(NOT trucks)” will hit on Documents 1 and 4. Applying the AND operator to these two searches (which is Search B) will hit on the documents that are common to these two searches, which is Document 4.
Please write a Python program to De Morgan's Law via document search.
You can use four text files as document 1, 2, 3 and 4.
```python
# Search A: NOT (cars OR trucks)
set A = list of all docs (a, b, c, d)
set B = list of docs with trucks (a, c)
set C = list of docs with cars (b, c)
set D = list of docs with cars or trucks (a, b, c)
set E = not D (d)
# Search B: (NOT cars) AND (NOT trucks)
set A = list of all docs (a, b, c, d)
set B = not cars (a,d)
set C = not trucks (b,d)
set D = (not Cars AND not Trucks) (d)
d = {}
d['cars'] = [a, c]
d['trucks'] = [b, c]
def get_word_count(word, file):
n = 0](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F10aa3c60-2143-49eb-a42e-7e7e91160ba4%2F0cecf033-867b-4e07-8cb4-c4a9a01793c5%2F40ebmn_processed.png&w=3840&q=75)
Transcribed Image Text:De Morgan’s laws commonly apply to text searching using Boolean operators AND, OR, and NOT. Consider a set of documents containing the words “cars” and “trucks”. De Morgan’s laws hold that these two searches will return the same set of documents:
**Search A:** NOT (cars OR trucks)
**Search B:** (NOT cars) AND (NOT trucks)
The corpus of documents containing “cars” or “trucks” can be represented by four documents:
1. Document 1: Contains only the word “cars”.
2. Document 2: Contains only “trucks”.
3. Document 3: Contains both “cars” and “trucks”.
4. Document 4: Contains neither “cars” nor “trucks”.
To evaluate Search A, clearly the search “(cars OR trucks)” will hit on Documents 1, 2, and 3. So the negation of that search (which is Search A) will hit everything else, which is Document 4.
Evaluating Search B, the search “(NOT cars)” will hit on documents that do not contain “cars”, which is Documents 2 and 4. Similarly the search “(NOT trucks)” will hit on Documents 1 and 4. Applying the AND operator to these two searches (which is Search B) will hit on the documents that are common to these two searches, which is Document 4.
Please write a Python program to De Morgan's Law via document search.
You can use four text files as document 1, 2, 3 and 4.
```python
# Search A: NOT (cars OR trucks)
set A = list of all docs (a, b, c, d)
set B = list of docs with trucks (a, c)
set C = list of docs with cars (b, c)
set D = list of docs with cars or trucks (a, b, c)
set E = not D (d)
# Search B: (NOT cars) AND (NOT trucks)
set A = list of all docs (a, b, c, d)
set B = not cars (a,d)
set C = not trucks (b,d)
set D = (not Cars AND not Trucks) (d)
d = {}
d['cars'] = [a, c]
d['trucks'] = [b, c]
def get_word_count(word, file):
n = 0
![**Code Snippet for Educational Purposes**
This Python script demonstrates the implementation of the **Single Responsibility Principle (SRP)** in software design. The SRP suggests dividing a main problem into smaller, logical sub-problems, solving each, and then using "glue code" to integrate these solutions into a functional program.
### Code Explanation:
1. **lines = x.readlines()**
- Reads lines from a file `x`.
2. **for line in lines:**
- Iterates over each line in the file.
3. **if line.find(word) > -1:**
- Checks if the `word` exists in the line.
4. **n+=1**
- Increments a counter `n` when the `word` is found.
5. **return n**
- Returns the count of lines containing the `word`.
### Main Function (`main()`):
1. **Read File**
- Reads and returns all lines from a file.
2. **Tokenize and Create Histogram**
- Tokenizes each line and creates a histogram (frequency count) for each word.
- Example:
- Initial Histogram: `h = {'this':[1, filename], 'is':[1, filename], 'my':[1, filename], 'car':[1, filename]}`
- Aggregated: `a = {'this':[2, [filename1, filename2]], 'is':[2, [filename1, filename2]], 'my':2, 'car':1, 'truck':1}`
3. **Aggregate Histograms**
- Combines histograms from different files.
4. **Glue Code**
- **Look for 'cars' and 'trucks'**:
```python
k = ['cars', 'trucks']
cars = []
trucks = []
if k[0] in a.keys():
cars += (a[k[0]][1])
if k[1] in a.keys():
trucks += (a[k[1]][1])
```
- This segment searches for the presence of 'cars' and 'trucks' within the aggregated data and appends occurrences to respective lists.
This example illustrates how breaking down a problem into smaller components and strategically recombining them facilitates a cleaner and more manageable code structure, embodying the essence of the SRP.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F10aa3c60-2143-49eb-a42e-7e7e91160ba4%2F0cecf033-867b-4e07-8cb4-c4a9a01793c5%2Fmjj4tip_processed.png&w=3840&q=75)
Transcribed Image Text:**Code Snippet for Educational Purposes**
This Python script demonstrates the implementation of the **Single Responsibility Principle (SRP)** in software design. The SRP suggests dividing a main problem into smaller, logical sub-problems, solving each, and then using "glue code" to integrate these solutions into a functional program.
### Code Explanation:
1. **lines = x.readlines()**
- Reads lines from a file `x`.
2. **for line in lines:**
- Iterates over each line in the file.
3. **if line.find(word) > -1:**
- Checks if the `word` exists in the line.
4. **n+=1**
- Increments a counter `n` when the `word` is found.
5. **return n**
- Returns the count of lines containing the `word`.
### Main Function (`main()`):
1. **Read File**
- Reads and returns all lines from a file.
2. **Tokenize and Create Histogram**
- Tokenizes each line and creates a histogram (frequency count) for each word.
- Example:
- Initial Histogram: `h = {'this':[1, filename], 'is':[1, filename], 'my':[1, filename], 'car':[1, filename]}`
- Aggregated: `a = {'this':[2, [filename1, filename2]], 'is':[2, [filename1, filename2]], 'my':2, 'car':1, 'truck':1}`
3. **Aggregate Histograms**
- Combines histograms from different files.
4. **Glue Code**
- **Look for 'cars' and 'trucks'**:
```python
k = ['cars', 'trucks']
cars = []
trucks = []
if k[0] in a.keys():
cars += (a[k[0]][1])
if k[1] in a.keys():
trucks += (a[k[1]][1])
```
- This segment searches for the presence of 'cars' and 'trucks' within the aggregated data and appends occurrences to respective lists.
This example illustrates how breaking down a problem into smaller components and strategically recombining them facilitates a cleaner and more manageable code structure, embodying the essence of the SRP.
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