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ããããã¢ãŒã ãå¶åŸ¡ããPythonã¹ã¯ãªãããå®è£
ããŠãã ããã | ```python
import pyfirmata
board = pyfirmata.Arduino('COM3')
it = pyfirmata.util.Iterator(board)
it.start()
arm = board.get_pin('d:10:s')
arm.write(90)
``` | 188 | [{"from": "human", "value": "ããããã¢ãŒã ãå¶åŸ¡ããPythonã¹ã¯ãªãããå®è£
ããŠãã ããã"}, {"from": "gpt", "value": "```python
import pyfirmata
board = pyfirmata.Arduino('COM3')
it = pyfirmata.util.Iterator(board)
it.start()
arm = board.get_pin('d:10:s')
arm.write(90)
```"}] |
|
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| ```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X, y)
``` | 223 | [{"from": "human", "value": "ã©ã³ãã ãã©ã¬ã¹ãã®åé¡åšãå®è£
ããŠãã ããã
"}, {"from": "gpt", "value": "```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X, y)
```"}] |
|
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ããŠãã ãã | ```python
import gym
env = gym.make('FrozenLake-v0')
state = env.reset()
for _ in range(1000):
action = env.action_space.sample()
state, reward, done, info = env.step(action)
if done:
break
``` | 243 | [{"from": "human", "value": "è¿·è·¯ã解ãããããçšã®åŒ·ååŠç¿ã¢ã«ãŽãªãºã ãå®è£
ããŠãã ãã"}, {"from": "gpt", "value": "```python
import gym
env = gym.make('FrozenLake-v0')
state = env.reset()
for _ in range(1000):
action = env.action_space.sample()
state, reward, done, info = env.step(action)
if done:
break
```"}] |
|
äºåæ³ã䜿çšããŠæ¹çšåŒã®æ ¹ãèŠã€ãåºãPythonã®é¢æ°ãäœæããŠãã ããã | ```python
def bisection(f, a, b, tol=1e-5):
c = (a + b) / 2.0
while abs(f(c)) > tol:
if f(c) * f(a) < 0:
b = c
else:
a = c
c = (a + b) / 2.0
return c
``` | 251 | [{"from": "human", "value": "äºåæ³ã䜿çšããŠæ¹çšåŒã®æ ¹ãèŠã€ãåºãPythonã®é¢æ°ãäœæããŠãã ããã"}, {"from": "gpt", "value": "```python
def bisection(f, a, b, tol=1e-5):
c = (a + b) / 2.0
while abs(f(c)) > tol:
if f(c) * f(a) < 0:
b = c
else:
a = c
c = (a + b) / 2.0
return c
```"}] |
|
Pythonã§ãWebã¹ã¯ã¬ã€ãã³ã°ãšããŒã¿æœåºããã³ãŒããäœæããŠãã ããã | ```python
import requests
from bs4 import BeautifulSoup
response = requests.get('https://example.com')
soup = BeautifulSoup(response.text, 'html.parser')
title = soup.find('title').get_text()
``` | 234 | [{"from": "human", "value": "Pythonã§ãWebã¹ã¯ã¬ã€ãã³ã°ãšããŒã¿æœåºããã³ãŒããäœæããŠãã ããã"}, {"from": "gpt", "value": "```python
import requests
from bs4 import BeautifulSoup
response = requests.get('https://example.com')
soup = BeautifulSoup(response.text, 'html.parser')
title = soup.find('title').get_text()
```"}] |
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ç»åèªç¥ã«äœ¿çšãããã¥ãŒã©ã«ãããã¯ãŒã¯ãOpenCVã§å®è£
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import cv2
net = cv2.dnn.readNetFromCaffe('deploy.prototxt', 'weights.caffemodel')
image = cv2.imread('image.jpg')
blob = cv2.dnn.blobFromImage(image, 1.0, (224, 224), (104.0, 117.0, 123.0))
net.setInput(blob)
preds = net.forward()
``` | 282 | [{"from": "human", "value": "ç»åèªç¥ã«äœ¿çšãããã¥ãŒã©ã«ãããã¯ãŒã¯ãOpenCVã§å®è£
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import cv2
net = cv2.dnn.readNetFromCaffe('deploy.prototxt', 'weights.caffemodel')
image = cv2.imread('image.jpg')
blob = cv2.dnn.blobFromImage(image, 1.0, (224, 224), (104.0, 117.0, 123.0))
net.setInput(blob)
preds = net.forward()
```"}] |
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ããåèªã®ã»ãã³ãã£ã¯ã¹ã®é¢ä¿ããã£ããã£ããŠãã ããã | ```python
from gensim.models import Word2Vec
sentences = [['machine', 'learning'], ['deep', 'learning'], ['natural', 'language', 'processing']]
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, sg=0)
model.wv.most_similar('machine')
``` | 314 | [{"from": "human", "value": "Gensimã䜿çšããŠWord2Vecã¢ãã«ãPythonã§å®è£
ããåèªã®ã»ãã³ãã£ã¯ã¹ã®é¢ä¿ããã£ããã£ããŠãã ããã"}, {"from": "gpt", "value": "```python
from gensim.models import Word2Vec
sentences = [['machine', 'learning'], ['deep', 'learning'], ['natural', 'language', 'processing']]
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, sg=0)
model.wv.most_similar('machine')
```"}] |
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from textblob import TextBlob
def translate_text(text, to_lang):
blob = TextBlob(text)
return blob.translate(to=to_lang)
# Example usage:
text = 'Hello World'
translated_text = translate_text(text, 'es')
print(translated_text)
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from textblob import TextBlob
def translate_text(text, to_lang):
blob = TextBlob(text)
return blob.translate(to=to_lang)
# Example usage:
text = 'Hello World'
translated_text = translate_text(text, 'es')
print(translated_text)
```"}] |
|
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人éã®èº«äœãæäœãã
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ç»ååŠçãè¡ã£ãŠã¿ãŸããããã¹ãã©ã€ããå転ãããæ¡å€§çž®å°ããé¢æ°ãå®è£
ããŠãã ããã | ```python
import pyglet
image = pyglet.image.load('example.png')
sprite = pyglet.sprite.Sprite(image)
sprite.rotation = 45
sprite.scale = 1.5
window = pyglet.window.Window()
@window.event
def on_draw():
window.clear()
sprite.draw()
pyglet.app.run()
``` | 306 | [{"from": "human", "value": "ç»ååŠçãè¡ã£ãŠã¿ãŸããããã¹ãã©ã€ããå転ãããæ¡å€§çž®å°ããé¢æ°ãå®è£
ããŠãã ããã"}, {"from": "gpt", "value": "```python
import pyglet
image = pyglet.image.load('example.png')
sprite = pyglet.sprite.Sprite(image)
sprite.rotation = 45
sprite.scale = 1.5
window = pyglet.window.Window()
@window.event
def on_draw():
window.clear()
sprite.draw()
pyglet.app.run()
```"}] |
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NLTKLibraryã䜿çšããŠã·ã³ãã«ãªãã£ããããããPythonã§å®è£
ããŠãã ããã | ```python
import nltk
from nltk.chat.util import Chat, reflections
pairs = [(r'hi', 'hello'), (r'how are you', 'I am fine')]
chatbot = Chat(pairs, reflections)
chatbot.converse()
``` | 227 | [{"from": "human", "value": "NLTKLibraryã䜿çšããŠã·ã³ãã«ãªãã£ããããããPythonã§å®è£
ããŠãã ããã"}, {"from": "gpt", "value": "```python
import nltk
from nltk.chat.util import Chat, reflections
pairs = [(r'hi', 'hello'), (r'how are you', 'I am fine')]
chatbot = Chat(pairs, reflections)
chatbot.converse()
```"}] |
|
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2. 人工ç¥èœãšäººéã®å
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3. å°çå€çåœäœãšã®ééã¯ãã©ã®ããã«å¯Ÿå¿ããã®ã§ããããïŒ | 137 | [{"from": "human", "value": "2050幎ã®å°æ¥ã®éœåžãèå°ã«ããã人ã
ãæ±ããã§ããã質åã3ã€äœæããŠãã ããã"}, {"from": "gpt", "value": "1. ããªãã®éœåžã§ã¯ããšãã«ã®ãŒã¯ã©ã®ããã«äŸçµŠãããŠããŸããïŒ
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ãè¡šçŸããæç« ãäœæããŠãã ããã"}, {"from": "gpt", "value": "圌ã®ç³ã¯ããŸãã§æ·±ãæµ·ã®åºã®ããã«ãæ²ãã¿ãšçµ¶æã«æºã¡æº¢ããŠããŸããã
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äž»æååæã«ããç¹åŸŽéã®æžå°ãPythonã§å®è£
ããŠãã ãã | ```python
import pyfirmata
board = pyfirmata.Arduino('COM3')
it = pyfirmata.util.Iterator(board)
it.start()
arm = board.get_pin('d:10:s')
arm.write(90)
``` | 185 | [{"from": "human", "value": "äž»æååæã«ããç¹åŸŽéã®æžå°ãPythonã§å®è£
ããŠãã ãã"}, {"from": "gpt", "value": "```python
import pyfirmata
board = pyfirmata.Arduino('COM3')
it = pyfirmata.util.Iterator(board)
it.start()
arm = board.get_pin('d:10:s')
arm.write(90)
```"}] |
|
ãŠãŒã©ãŒæ³ã䜿çšããŠæ®éçãªåŸ®åæ¹çšåŒã解ãPythonã®é¢æ°ãäœæããŠãã ããã | ```python
def euler_method(f, y0, t0, t1, h):
y, t = y0, t0
while t <= t1:
y += h * f(t, y)
t += h
return y
``` | 180 | [{"from": "human", "value": "ãŠãŒã©ãŒæ³ã䜿çšããŠæ®éçãªåŸ®åæ¹çšåŒã解ãPythonã®é¢æ°ãäœæããŠãã ããã"}, {"from": "gpt", "value": "```python
def euler_method(f, y0, t0, t1, h):
y, t = y0, t0
while t <= t1:
y += h * f(t, y)
t += h
return y
```"}] |
|
ç«ããèªåã ãã®æ¹æ³ã§ã±ãŒããçŒãç©èªãæžããŠãã ããã | ã¢ã«ã¯ã人éã®ããã«æããªãã®ã§ãã±ãŒããçŒãããšãã§ããŸããã
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ããã¯ãèŠãç®ãå³ããä»ã®ç«ãäœã£ãããšã®ãªãããŠããŒã¯ãªã±ãŒãã§ããã"}] |
|
äºå€åé¡ã«äœ¿çšããSupport Vector Machineãå®è£
ããŠãã ããã | ```python
from sklearn import svm
X = [[0, 0], [1, 1]]
y = [0, 1]
clf = svm.SVC()
clf.fit(X, y)
``` | 140 | [{"from": "human", "value": "äºå€åé¡ã«äœ¿çšããSupport Vector Machineãå®è£
ããŠãã ããã"}, {"from": "gpt", "value": "```python
from sklearn import svm
X = [[0, 0], [1, 1]]
y = [0, 1]
clf = svm.SVC()
clf.fit(X, y)
```"}] |
|
Pythonã§Isolation Forest ã䜿çšããã¹ã¯ãªãããå®è£
ããŠãã ããã | ```python
from sklearn.ensemble import IsolationForest
X = [[0, 1], [0, -1], [-1, 0], [1, 0], [0, 0]]
clf = IsolationForest().fit(X)
``` | 180 | [{"from": "human", "value": "Pythonã§Isolation Forest ã䜿çšããã¹ã¯ãªãããå®è£
ããŠãã ããã"}, {"from": "gpt", "value": "```python
from sklearn.ensemble import IsolationForest
X = [[0, 1], [0, -1], [-1, 0], [1, 0], [0, 0]]
clf = IsolationForest().fit(X)
```"}] |
|
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ã®ã¹ãã«ãã¹ãä¿®æ£ããããã¹ãæ ¡æ£æ©èœãPythonã§å®è£
ããŠãã ããã | ```python
from textblob import TextBlob
incorrect_text = 'Ths is an incmplete sentece.'
blob = TextBlob(incorrect_text)
corrected_text = blob.correct()
print(f'Corrected Text: {corrected_text}')
``` | 244 | [{"from": "human", "value": "äžããããããã¹ãå
ã®ã¹ãã«ãã¹ãä¿®æ£ããããã¹ãæ ¡æ£æ©èœãPythonã§å®è£
ããŠãã ããã"}, {"from": "gpt", "value": "```python
from textblob import TextBlob
incorrect_text = 'Ths is an incmplete sentece.'
blob = TextBlob(incorrect_text)
corrected_text = blob.correct()
print(f'Corrected Text: {corrected_text}')
```"}] |
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ããŠãã ãããããŒã¿ã»ããã«ã¯ã'Age'ã'EstimatedSalary'ã'LoanAmount'ãšããã«ã©ã ãå«ãŸããŠããŸããã¿ãŒã²ããã«ã©ã ã¯'Loan\_Status'ã§ãã | ```python
from sklearn.tree import DecisionTreeClassifier
X = [[0, 0], [1, 1]]
y = [0, 1]
clf = DecisionTreeClassifier()
clf = clf.fit(X, y)
``` | 302 | [{"from": "human", "value": "ããŒã³ã®æ¹åãäºæž¬ãã決å®æšåé¡åšãå®è£
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from sklearn.tree import DecisionTreeClassifier
X = [[0, 0], [1, 1]]
y = [0, 1]
clf = DecisionTreeClassifier()
clf = clf.fit(X, y)
```"}] |
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import gym
env = gym.make('FrozenLake-v0')
state = env.reset()
for _ in range(1000):
action = env.action_space.sample()
state, reward, done, info = env.step(action)
if done:
break
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import gym
env = gym.make('FrozenLake-v0')
state = env.reset()
for _ in range(1000):
action = env.action_space.sample()
state, reward, done, info = env.step(action)
if done:
break
```"}] |
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from sklearn.neighbors import KNeighborsClassifier
X = [[0], [1], [2], [3]]
y = [0, 0, 1, 1]
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(X, y)
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ããŠãã ãã"}, {"from": "gpt", "value": "```python
from sklearn.neighbors import KNeighborsClassifier
X = [[0], [1], [2], [3]]
y = [0, 0, 1, 1]
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(X, y)
```"}] |
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from sklearn.linear_model import LogisticRegression
X = [[0, 1], [1, 0], [2, 2]]
y = [0, 1, 0]
clf = LogisticRegression().fit(X, y)
``` | 167 | [{"from": "human", "value": "ããžã¹ãã£ãã¯ååž°ã¢ãã«ãå®è£
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from sklearn.linear_model import LogisticRegression
X = [[0, 1], [1, 0], [2, 2]]
y = [0, 1, 0]
clf = LogisticRegression().fit(X, y)
```"}] |
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