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``bash
/bin/bash
# å
¥åãã©ã«ããèšå®ãã (ããã©ã«ã㯠~/Downloads)
input_folder="${1:-$HOME/Downloads}"
# ã«ããŽãªãã©ã«ããååšããªãå Žåã¯äœæãã
mkdir -p "${input_folder}/Documents"
mkdir -p "${input_folder}/Torrents"
mkdir -p "${input_folder}/Music"
mkdir -p "${input_folder}/Videos"
mkdir -p "${input_folder}/Programs"
# ãã¡ã€ã«ãããããã®ã«ããŽãªãŒãã©ã«ããŒã«ç§»åãã
find "$input_folder" -type f -print0 | while IFS= read -r -d '' file; do
case "$(file -b --mime-type "$file")" in
application/pdf|text/*)
mv "$file" "${input_folder}/Documents".
;;
ã¢ããªã±ãŒã·ã§ã³/x-bittorrent)
mv "$file" "${input_folder}/Torrents".
;;
audio/*|*/ogg)
mv "$file" "${input_folder}/Music" ;;; ãªãŒãã£ãª/*|*/ogg)
;;
video/*)
mv "$file" "${input_folder}/Videos".
;;
ã¢ããªã±ãŒã·ã§ã³/*)
mv "$file" "${input_folder}/Programs".
;;
å®è¡
å®äº
```
ãã®ã¹ã¯ãªãããæ¯æ¥æ£åã«å®è¡ããcronjobãšããŠã»ããã¢ããããã«ã¯ã以äžã®æé ã«åŸãïŒ
1.ã¹ã¯ãªããããã¡ã€ã«ã«ä¿åããŸããäŸãã°ãfile_classification.shã§ãã
2.2. chmod +x file_classification.shãå®è¡ããŠãã¹ã¯ãªãããå®è¡å¯èœãªç¶æ
ã«ããŸãã
3.crontab -eãå®è¡ããŠãç·šéçšã®crontabãéãã
4.4. 以äžã®è¡ãcrontabãã¡ã€ã«ã«è¿œå ããŸãïŒ
``Crontab
0 12 * * /path/to/file_classification.sh
```
/path/to/file_classification.sh ã¯ãã¹ã¯ãªãããä¿åããå®éã®ãã¹ã«çœ®ãæããŠãã ããã
5.crontabãã¡ã€ã«ãä¿åãããšãã£ã¿ãŒãçµäºããŸãã
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ã®ãã¡ã€ã«ãåé¡ããŸãã
path/to/file_classification.shã¯ãã¹ã¯ãªãããä¿åããå®éã®ãã¹ã«çœ®ãæããŠãã ããããŸããå¥ã®ã«ããŽãªãŒãã©ã«ããŒã»ããã䜿çšããããã«ããŽãªãŒã«ãã¡ã€ã«ã¿ã€ããè¿œå ãããå Žåã¯ãã¹ã¯ãªãããä¿®æ£ããŠãã ããã
### æ瀺:
ã§ã¯ãããããã€ãœã³ã»ã¹ã¯ãªããã«æžãæããŠã¿ããã | null | ãã¡ããã§ãïŒä»¥äžã¯Pythonã§æžãããBashã¹ã¯ãªããã§ãã
```
import os
import shutil
import mimetypes
from pathlib import Path
# å
¥åãã©ã«ããèšå®ãã (ããã©ã«ã㯠~/Downloads)
input_folder = Path(os.environ.get('1', os.path.join(os.environ['HOME'], 'Downloads')))
# ã«ããŽãªãã©ã«ããååšããªãå Žåã¯äœæãã
(input_folder / 'Documents').mkdir(exist_ok=True)
(input_folder / 'Torrents').mkdir(exist_ok=True)
(input_folder / 'Music').mkdir(exist_ok=True)
(input_folder / 'Videos').mkdir(exist_ok=True)
(input_folder / 'Programs').mkdir(exist_ok=True)
# ãã¡ã€ã«ãããããã®ã«ããŽãªãŒãã©ã«ãã«ç§»å
for file in input_folder.glob('*')ïŒ
if file.is_file()ïŒ
mime_type, _ = mimetypes.guess_type(file)
if mime_typeïŒ
if mime_type.startswith('application/pdf') or mime_type.startswith('text')ïŒ
shutil.move(str(file), str(input_folder / 'Documents'))
elif mime_type.startswith('application/x-bittorrent')ïŒ
shutil.move(str(file), str(input_folder / 'Torrents'))
elif mime_type.startswith('audio') or mime_type.endswith('/ogg')ïŒ
shutil.move(str(file), str(input_folder / 'Music'))
elif mime_type.startswith('video')ïŒ
shutil.move(str(file), str(input_folder / 'Videos'))
elif mime_type.startswith('application')ïŒ
shutil.move(str(file), str(input_folder / 'Programs'))
``` |
ããçºã§ã®å¹Žééæ°Žéã瀺ãã°ã©ããèªã¿ããã¬ã³ããèšè¿°ããŠãã ããã | null | ã°ã©ããèŠããšãçºã®å¹Žééæ°Žéã¯ã1950幎ãã2000幎ãŸã§ã®æéã§ããããå¢å åŸåã«ããããšãããããŸãããã®åŸã2000幎ãã2010幎ãŸã§ã¯ããæžå°åŸåã«ãããŸããããã®åŸã¯åã³å¢å ããçŸåšã«è³ããŸãããŸãã1990幎代åã°ä»¥éã幎ééæ°Žéã2000mmãè¶
ãã幎ãå¢ããŠããããšãããããŸãããã®ããšããããã®çºã§ã¯å
šäœãšããŠå¹Žééæ°Žéãå¢å ããŠãããšèšããã§ãããã |
é¡æžïŒåæšç»é²é¡ïŒãäœæããéã«ãåºé¡äººããé¡äŒŒçŸ€ã³ãŒããèšèŒããå¿
èŠã¯ãããŸããïŒ | null | é¡äŒŒçŸ€ã³ãŒããé¡æžã«èšèŒããå¿
èŠã¯ãããŸãããé¡äŒŒçŸ€ã³ãŒãã¯ãæ€çŽ¢ããŒçãšããŠçšãããããç¹èš±åºã®å¯©æ»å®ãæå®ååã»æå®åœ¹åã«ä»äžãããã®ã§ãã®ã§ãåºé¡ãããéãé¡æžïŒåæšç»é²é¡ïŒã«ã¯èšèŒããªãã§ãã ããã
åèïŒç¹èš±åºHP æ¥æ¬ã«ããããé¡äŒŒçŸ€ã³ãŒããã«ã€ããŠ
https://www.jpo.go.jp/system/trademark/gaiyo/bunrui/ruijigun_cord_reidai.html |
äžããããSQLããŒã¿ããŒã¹ããç¹å®ã®ããŒã¿ãååŸããPythonã³ãŒããèšè¿°ããŠãã ãããæ¡ä»¶ã¯ã'name'ã'Tom'ã§ããè¡ãååŸããããšã§ãã | ```sql
CREATE TABLE users (
id INTEGER PRIMARY KEY,
name TEXT NOT NULL,
age INTEGER NOT NULL
);
INSERT INTO users (name, age) VALUES ('Tom', 20);
INSERT INTO users (name, age) VALUES ('Jerry', 18);
INSERT INTO users (name, age) VALUES ('Mike', 25);
``` | ```python
import sqlite3
# connect to the SQLite database
conn = sqlite3.connect('my_database.db')
# create a cursor object
cursor = conn.cursor()
# execute the SQL query
cursor.execute('SELECT * FROM users WHERE name=?', ('Tom',))
# fetch the results
results = cursor.fetchall()
# print the results
for row in results:
print(row)
# close the connection
conn.close()
``` |
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number_of_student_tickets = 12
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cost_of_teacher_ticket = 3
# cost of student and teacher tickets
cost_of_student_and_teacher_tickets = number_of_student_tickets * cost_of_student_ticket + number_of_teacher_tickets * cost_of_teacher_ticket
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image_file = "sample_image.jpg"
``` | ```python
from PIL import Image
def get_image_size(file):
img = Image.open(file)
width, height = img.size
return (width, height)
print(get_image_size(image_file))
```
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```
(800, 600)
``` |
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äžããããæç« ã®äžããããŒã¯ãŒããæœåºããŠãã ãããããŒã¯ãŒãã¯ãäžããããæç« ã®äžã§æ³šç®ãã¹ãéèŠãªåèªã§ãã | æ±äº¬éœå
ã§æãé«ããã«ã¯æ±äº¬ãããã¿ãŠã³ã¿ã¯ãŒã§ãé«ãã¯248mã§ãã | ãæ±äº¬éœå
ã§æãé«ããã«ãããæ±äº¬ãããã¿ãŠã³ã¿ã¯ãŒããã248mã |
次㮠SQL ã¯ãšãªã䜿çšããŠãããŒã¿ããŒã¹ããæ
å ±ãååŸããPython ããã°ã©ã ãèšè¿°ããŠãã ããã | ```sql
SELECT * FROM users WHERE age >= 18;
``` | ```python
import psycopg2
def get_adult_users():
conn = psycopg2.connect(database="your_database", user="your_user", password="your_password", host="your_host", port="your_port")
cur = conn.cursor()
cur.execute("SELECT * FROM users WHERE age >= 18;")
rows = cur.fetchall()
cur.close()
conn.close()
return rows
``` |
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æç³»åããŒã¿ã®ã¢ãããªãŒãèŠã€ããæ°åŠçæ¹æ³ã«ã¯ã©ã®ãããªãã®ããããŸããïŒãŸããã©ã®ãããªç§åŠè«æãæšèŠã§ããŸããïŒ | null | æç³»åããŒã¿ã®ã¢ãããªãŒãèŠã€ããæ°åŠçææ³ã¯ããã€ããããŸãïŒ
1.çµ±èšçæ¹æ³ïŒãã®æ¹æ³ã«ã¯ãZã¹ã³ã¢ãGrubbsã®æ€å®ãBoxplotæ³ãªã©ã®ãã¯ããã¯ãå«ãŸããŸãã
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ãã®ãããã¯ã«é¢é£ããç§åŠçåºçç©ã®ããã€ãã¯ä»¥äžã®éãã§ããïŒ
1.S. Chandola, A. Banerjee, and V. Kumarè "Anomaly Detection Techniques for Time Series Data".
2.ãããã¹ãã¹ããªãŒã ããŒã¿ã«ãããç°åžžæ€ç¥æè¡ã®ãµãŒãã€" M. Mohiuddin, H. Yu, and O. Ranaè.
3.ãæç³»åããŒã¿ã«å¯Ÿããããã¹ããªç°åžžæ€ç¥ã D. DuaãP. Kumarèã
4.ãA Machine Learning Approach for Anomaly Detection in Time Series Data" by A. Ahmed, A. Mahmood, and M. Waseem.
5.ããŠã§ãŒãã¬ããã«åºã¥ãæç³»åã®ç°åžžæ€åºãšåé¡ã C. ChenãY. LiãX. Liangèã |
次ã®Pythonã³ãŒãã«ãã°ãå«ãŸããŠããŸãããã°ãèŠã€ããŠãä¿®æ£ããã³ãŒããæ瀺ããŠãã ããã | ```python
def add(a, b):
return a + b
result = add("1", "2")
``` | ```python
def add(a, b):
return int(a) + int(b)
result = add("1", "2")
``` |
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### æ瀺:
ãããçŸå®åé¡ãšããŠãããªããã¡ãããªããã¡ã®åå«ãæŽãåºãã人éã«åæãç¿»ãã®ãé²ãã«ã¯ã©ããããããã®ã§ãããïŒããªããã¡ãçœå¶ããããã«ãç§ãã¡ãæ¡çšã§ããæŠç¥ã¯ããã®ã§ããããïŒ | null | ãããŒãã»ãã€ã«ãºã®ã¬ã¯ãã£ãŒã·ãªãŒãºïŒhttps://www.youtube.com/@RobertMilesAI ïŒã«ãããšã2023幎2æçŸåšãçŸåšã®ç§åŠã§ã¯ãæ±çšAIãæŽèµ°ããããèªèº«ã®ç®çïŒãçãæ®ããããïŒã®ããããã«å¥ä»ãããªã¹ã¯ã«å¯Ÿããçããæã£ãŠããªãã
ããããããŸãèšèšããããå®å
šã ãšæããããæ±çšAIãã䞻人ã®ç®çãæããããã«éåžžã«å¹ççã«ãªããå°çäžã®ä»ã®*ãã¹ãŠã®äººã®ç掻ãéåžžã«å°é£ã«ããå¯èœæ§ã¯ãŸã é«ãã
ãã®çãã圹ã«ç«ã€ããšãé¡ã£ãŠããã |
以äžã®ããã«ãæéã»ã°ã¡ã³ããåãåããéãªã£ããçµåããPythoné¢æ°ãäœãïŒ[[0, 10], [8, 20], [25, 30]] => [[0, 20], [25, 30]]
### å¿ç:
以äžã¯ãæéã»ã°ã¡ã³ãã®ãªã¹ããåãåããããããéãªã£ãŠããå Žåã«ããããçµåããPythoné¢æ°ã§ãïŒ
``py
def combine_time_segments(segments)ïŒ
# ã»ã°ã¡ã³ããéå§æå»é ã«ãœãŒããã
segments.sort(key=lambda x: x[0])
# çµåããã»ã°ã¡ã³ããæ ŒçŽãããªã¹ããåæåãã
combined_segments = [].
# çŸåšã®ã»ã°ã¡ã³ããæ ŒçŽããå€æ°ãåæåãã
current_segment = segments[0
# æ®ãã®ã»ã°ã¡ã³ããã«ãŒããã
for segment in segments[1:]ïŒ
# çŸåšã®ã»ã°ã¡ã³ãã®éå§æå»ãã次ã®ã»ã°ã¡ã³ãã®çµäºæå»ä»¥äžã§ããå Žåã
# éè€ããŠãããããããŒãžããå¿
èŠããã
if current_segment[1] >= segment[0]ïŒ
# çŸåšã®ã»ã°ã¡ã³ãã®çµäºæå»ããçŸåšã®çµäºæå»ãšæ¬¡ã®ã»ã°ã¡ã³ãã®çµäºæå»ã® # æ倧å€ã«æŽæ°ããã
# 次ã®ã»ã°ã¡ã³ãã®çµäºæå»ã®æ倧å€ã«æŽæ°ãã
ã«ã¬ã³ãã»ã°ã¡ã³ã[1] = max(current_segment[1], segment[1])
elseïŒ
# ã»ã°ã¡ã³ãã¯éãªããªãã®ã§ãçŸåšã®ã»ã°ã¡ã³ããçµåãããã»ã°ã¡ã³ãã®ãªã¹ãã« # è¿œå ãã次ã®ã»ã°ã¡ã³ããçŸåšã®ã»ã°ã¡ã³ããšããã
# 次ã®ã»ã°ã¡ã³ããçŸåšã®ã»ã°ã¡ã³ããšãã
combined_segments.append(current_segment)
çŸåšã®ã»ã°ã¡ã³ã = ã»ã°ã¡ã³ã
# çµåãããã»ã°ã¡ã³ãã®ãªã¹ãã«æåŸã®ã»ã°ã¡ã³ããè¿œå ãã
combined_segments.append(current_segment)
return çµåãããã»ã°ã¡ã³ã
```
以äžã«äœ¿çšäŸã瀺ããŸãïŒ
``py
segments = [[0, 10], [8, 20], [25, 30]] ã®ããã«ããã
combined_segments = combine_time_segments(segments)
print(combined_segments)
```
ããã§åºåãããã®ã¯`[[0, 20], [25, 30]]`
### æ瀺:
ã³ãŒããããããšããcombine_time_segments`é¢æ°ã®ãã¹ããè¿œå ããŠããããŸããïŒ | null | ãã¡ãããcombine_time_segmentsé¢æ°ããã¹ãããããã«äœ¿çšã§ãããã¹ãã±ãŒã¹ãããã€ã玹ä»ãããïŒ
# ãã¹ãã±ãŒã¹ 1: éãªããªãã»ã°ã¡ã³ã
segments1 = [[0, 5], [10, 15], [20, 25]] ãšããã
assert combine_time_segments(segments1) == [[0, 5], [10, 15], [20, 25]].
# ãã¹ãã±ãŒã¹ 2: éãªã£ãŠããã»ã°ã¡ã³ããããŒãžããå¿
èŠããã
segments2 = [[0, 10], [8, 20], [25, 30]] # ãã¹ãã±ãŒã¹2ïŒããŒãžãå¿
èŠãªéè€ã»ã°ã¡ã³ãã
assert combine_time_segments(segments2) == [[0, 20], [25, 30]].
# ãã¹ãã±ãŒã¹ 3: éãªã£ãŠããã»ã°ã¡ã³ããå¥ã®é åºã§ããŒãžããå Žå
segments3 = [[25, 30], [0, 10], [8, 20]] # ãã¹ãã±ãŒã¹ 3: éãªã£ãŠããã»ã°ã¡ã³ããå¥ã®é åºã§ããŒãžããã
assert combine_time_segments(segments3) == [[0, 20], [25, 30]].
# ãã¹ãã±ãŒã¹ 4: 1 ã€ã®ã»ã°ã¡ã³ã
ã»ã°ã¡ã³ã4 = [[0, 10]] # ãã¹ãã±ãŒã¹4ïŒ1ã»ã°ã¡ã³ã
assert combine_time_segments(segments4) == [[0, 10]].
# ãã¹ãã±ãŒã¹ 5: éãªãåã 2 ã€ã®ã»ã°ã¡ã³ã
segments5 = [[0, 10], [5, 15]] # ãã¹ãã±ãŒã¹5ïŒéãªãåã2ã€ã®ã»ã°ã¡ã³ãã
assert combine_time_segments(segments5) == [[0, 15]].
ãããã®ãã¹ãã±ãŒã¹ã test_combine_time_segments ãšããå¥ã®é¢æ°ã«è¿œå ãã combine_time_segments é¢æ°ãæ£ããåäœããŠããããšã確èªããããã«åŒã³åºããŸããäŸãã°
def test_combine_time_segments()ïŒ
segments1 = [[0, 5], [10, 15], [20, 25]] ãšããã
assert combine_time_segments(segments1) == [[0, 5], [10, 15], [20, 25]].
segments2 = [[0, 10], [8, 20], [25, 30]].
assert combine_time_segments(segments2) == [[0, 20], [25, 30]].
segments3 = [[25, 30], [0, 10], [8, 20]].
assert combine_time_segments(segments3) == [[0, 20], [25, 30]].
segments4 = [[0, 10]] ãšããã
assert combine_time_segments(segments4) == [[0, 10]].
segments5 = [[0, 10], [5, 15]] ãšããã
assert combine_time_segments(segments5) == [[0, 15]].
test_combine_time_segments()
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Pythonã³ãŒãã䜿çšããŠãã®åé¡ã解決ããŸãããã<llm-code>
lake_length = 10 / 10
lake_width = 10 / 10
total_time = 2 + 1 / 2
total_time_in_minutes = total_time * 60
time_for_length = 2 * 60
time_for_width = 1 * 60
lake_area = 10 / 10 * 10 / 10
lake_area
</llm-code><llm-code-output>
1.0
</llm-code-output>
æ¹ã®æ£æ¹åœ¢ã¯ã\\Boxed{1}å¹³æ¹ãã€ã«ã®é åãæã€æ£æ¹åœ¢ã§ãã |
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<numbers>
<number>1</number>
<number>2</number>
<number>3</number>
<number>4</number>
<number>5</number>
</numbers>
``` | ```python
import xml.etree.ElementTree as ET
def extract_numbers(xml_data):
root = ET.fromstring(xml_data)
numbers = []
for number in root.findall('number'):
numbers.append(int(number.text))
return numbers
``` |
äžãããã requiring_friend ãšããå€æ°ã«é¢ããŠããã®æå³ããããšãæ¥æ¬èªã§èª¬æããŠãã ããã | requiring_friend = True | requiring_friend ãšããå€æ°ã True ã«èšå®ãããŠãããšããããšã¯ããã®å€æ°ã¯åéãå¿
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```c#
float MapDistanceToRange(float distance)
{
// è·é¢å€ãç¯å²å
ãã©ããããã§ãã¯
if (è·é¢ < 200f || è·é¢ > 300f)
{
Debug.LogError("Distance value is out of range.")ïŒ
0f ãè¿ããŸãïŒ
}
// è·é¢å€ã0ïœ1ã®ç¯å²ã«ããããã
float mappedValue = (distance - 200f) / 100fïŒ
return mappedValueïŒ
}
```
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numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
``` | ```python
def extract_even_numbers(numbers):
return [num for num in numbers if num % 2 == 0]
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = extract_even_numbers(numbers)
print(even_numbers)
``` |
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# 8 days for 3 builders
builders = 3
days = 8
# the same size cottage
cottage_size = builders * days
# 6 builders working at the same rate
builders_6 = 6
# since the rate is the same the time it takes will be smaller
# for 6 builders to build the same size cottage
days_for_6_builders = cottage_size / builders_6
days_for_6_builders
</llm-code><llm-code-output>
4.0
</llm-code-output>
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# MongoDB ãžã®æ¥ç¶èšå®
client = pymongo.MongoClient("localhost", 27017)
db = client["your_database_name"]
collection = db["your_collection_name"]
# ããŒã¿ãã¬ãŒã ã®äŸ (Pandas ã䜿çš)
import pandas as pd
df1 = pd.DataFrame({
"name": ["John", "Jane", "Joe"],
"age": [28, 26, 32],
"city": ["New York", "Chicago", "Miami"]
})
df2 = pd.DataFrame({
"product": ["Apple", "Banana", "Orange"],
"price": [1, 2, 3],
"quantity": [10, 20, 30]
})
# ããŒã¿ãã¬ãŒã ã MongoDB ã®ã³ã¬ã¯ã·ã§ã³ã«æ¿å
¥
def insert_dataframe_to_mongo(df, collection_name):
db[collection_name].insert_many(df.to_dict("records"))
insert_dataframe_to_mongo(df1, "your_collection_name1")
insert_dataframe_to_mongo(df2, "your_collection_name2")
# ããŒã¿ã®ç¢ºèª
cursor = db.collection.find()
for document in cursor:
print(document)` |
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\\ begin {åŒ*}
\\ begin {aligned}
\\ frac {\\ sqrt {3x+5}} {\\ sqrt {6x+5}}ïŒ= \\ frac {\\ sqrt {5}} {3} \\\\
\\ frac {\\ sqrt {3x+5}} {\\ sqrt {6x+5}} - \\ frac {\\ sqrt {5}} {3}ïŒ= 0
\\ end {aligned}
\\ end {åŒ*}
äžè¬çãªåæ¯ã䜿çšããŠãã®å²åãç°¡çŽ åããŸããã$ 3 \\ sqrt {6x+5} $
\\ begin {åŒ*}
\\ begin {aligned}
\\ frac {3 \\ sqrt {3x+5} - \\ sqrt {5} \\ sqrt {6x+5}} {3 \\ sqrt {6x+5}}ïŒ= 0
\\ end {aligned}
\\ end {åŒ*}
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ååããŒãã®å Žåã«ã®ã¿ãåæ°ã¯ãŒãã«çããããã以äžãæžãããšãã§ããŸã
\\ begin {åŒ*}
\\ begin {aligned}
3 \\ sqrt {3x+5} - \\ sqrt {5} \\ sqrt {6x+5}ïŒ= 0 \\\\
3 \\ sqrt {3x+5}ïŒ= \\ sqrt {5} \\ sqrt {6x+5} \\\\
ïŒ3 \\ sqrt {3x+5}ïŒ^2ïŒ=ïŒ\\ sqrt {5} \\ sqrt {6x+5}ïŒ^2 \\\\
9ïŒ3x+5ïŒïŒ= 5ïŒ6x+5ïŒ\\\\
9 \\ times 3x + 9 \\ times 5ïŒ= 5 \\ times 6x + 5 \\ times 5 \\\\
27x + 45ïŒ= 30x + 25 \\\\
27x -30xïŒ= 25-45 \\\\
-3xïŒ= -20 \\\\
xïŒ= \\ frac {-20} {-3} \\\\
xïŒ= \\ boxed {\\ frac {20} {3}}
\\ end {aligned} |
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```python
x = 3*y + 2*z
``` |
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url = "https://example.com/data.csv"
``` | ```python
import pandas as pd
# Download the CSV file from the URL
data = pd.read_csv(url)
# Print the DataFrame
print(data)
``` |
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angle = rad(150) # convert angle to radians
# Calculate the values
result = cos(angle)
result2 = sin(angle) / tan(angle)
# Display the results
print("cos(150 degrees) =", result)
print("sin(150 degrees) / tan(150 degrees) =", result2)
</llm-code><llm-code-output>
cosïŒ150床ïŒ= -sqrtïŒ3ïŒ/2
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</llm-code-output>
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