import cvxpy as cp
import numpy as np
import matplotlib.pyplot as plt

# 変数を定義
x = cp.Variable()
y = cp.Variable()

# 目的関数と制約条件を定義
objective = cp.Minimize(3*x + 4*y)
constraints = [x + 2*y >= 10, 3*x - y <= 12, x >= 0, y >= 0]

# 最適化問題の定義
problem = cp.Problem(objective, constraints)

# 問題を解く
problem.solve()

# 最適な解の表示
print("Optimal value:", problem.value)
print("x =", x.value)
print("y =", y.value)

# グラフ描画
x_values = np.linspace(0, 10, 100)
y1_values = (10 - x_values) / 2
y2_values = 3 * x_values - 12

plt.figure(figsize=(6, 6))
plt.plot(x_values, y1_values, label='x + 2y >= 10')
plt.plot(x_values, y2_values, label='3x - y <= 12')
plt.fill_between(x_values, np.maximum(0, y1_values), np.minimum(10, (12 + y2_values) / 3), color='gray', alpha=0.3)
plt.plot(x.value, y.value, 'ro')  # 最適解のプロット
plt.xlabel('x')
plt.ylabel('y')
plt.title('Optimization with CVXPY')
plt.legend()
plt.grid(True)
plt.show()

import cvxpy as cp
import numpy as np
import matplotlib.pyplot as plt

x = cp.Variable()
y = cp.Variable()

objective = cp.Minimize(3*x + 4*y)
constraints = [x + 2*y >= 10, 3*x - y <= 12, x >= 0, y >= 0]

problem = cp.Problem(objective, constraints)

problem.solve()

print("Optimal value:", problem.value)
print("x =", x.value)
print("y =", y.value)

x_values = np.linspace(0, 10, 100)
y1_values = (10 - x_values) / 2
y2_values = 3 * x_values - 12

plt.figure(figsize=(6, 6))
plt.plot(x_values, y1_values, label='x + 2y >= 10')
plt.plot(x_values, y2_values, label='3x - y <= 12')
plt.fill_between(x_values, np.maximum(0, y1_values), np.minimum(10, (12 + y2_values) / 3), color='gray', alpha=0.3)
plt.plot(x.value, y.value, 'ro')  # 最適解のプロット
plt.xlabel('x')
plt.ylabel('y')
plt.title('Optimization with CVXPY')
plt.legend()
plt.grid(True)
plt.show()

from pulp import *

# ビンの数とアイテムの数を設定
bin_count = 2
item_count = 5

# 各ビンの容量、アイテムの重さを設定
bin_capacity = [10, 10]
item_weight = [2, 5, 4, 7, 1]

# 問題設定
prob = LpProblem("BinPacking", LpMinimize)

# アイテムをビンに入れるかどうかを示すノードを作成（バイナリ変数）
x = [[LpVariable("x(%s,%s)" % (i, j), cat="Binary") for j in range(item_count)] for i in range(bin_count)]

# 目的関数: 使用するビンの数を最小化する
prob += lpSum(x[i][j] for i in range(bin_count) for j in range(item_count))

# 制約条件: 各アイテムは1つのビンにしか入らない
for j in range(item_count):
    prob += lpSum(x[i][j] for i in range(bin_count)) == 1

# 制約条件: 各ビンの容量を超えてアイテムを入れられない
for i in range(bin_count):
    prob += lpSum(item_weight[j] * x[i][j] for j in range(item_count)) <= bin_capacity[i]

# 最適化計算
prob.solve()

# 結果表示
for i in range(bin_count):
    print("*****Bin", i, "*****")
    total_weight = 0
    for j in range(item_count):
        if value(x[i][j])==1:
            print("Item", j)
            total_weight+=item_weight[j]
    print("Total weight:", total_weight)

from pulp import *

bin_count = 2
item_count = 5
bin_capacity = [10, 10]
item_weight = [2, 5, 4, 7, 1]

prob = LpProblem("BinPacking", LpMinimize)

x = [[LpVariable("x(%s,%s)" % (i, j), cat="Binary") for j in range(item_count)] for i in range(bin_count)]

prob += lpSum(x[i][j] for i in range(bin_count) for j in range(item_count))

for j in range(item_count):
    prob += lpSum(x[i][j] for i in range(bin_count)) == 1

for i in range(bin_count):
    prob += lpSum(item_weight[j] * x[i][j] for j in range(item_count)) <= bin_capacity[i]

prob.solve()

for i in range(bin_count):
    print("*****Bin", i, "*****")
    total_weight = 0
    for j in range(item_count):
        if value(x[i][j])==1:
            print("Item", j)
            total_weight+=item_weight[j]
    print("Total weight:", total_weight)

import pandas as pd
import numpy as np
from prophet import Prophet
import matplotlib.pyplot as plt

# ランダムな株価データを生成
np.random.seed(42)
dates = pd.date_range(start='2023-01-01', periods=365, freq='D')
prices = np.cumsum(np.random.randn(365)) + 100  # ランダムな価格変動

data = {
    'ds': dates,
    'y': prices
}

df = pd.DataFrame(data)

# Prophetモデルの作成と学習
model = Prophet()
model.fit(df)

# 予測用データの作成
future = model.make_future_dataframe(periods=30)  # 30日間の予測
forecast = model.predict(future)

# 予測結果をグラフ化
fig = model.plot(forecast)
plt.xlabel('Date')
plt.ylabel('Stock Price')
plt.title('Random Stock Price Forecast')
plt.show()

np.random.seed(42)
dates = pd.date_range(start='2023-01-01', periods=365, freq='D')
prices = np.cumsum(np.random.randn(365)) + 100  # ランダムな価格変動

data = {
    'ds': dates,
    'y': prices
}

df = pd.DataFrame(data)

model = Prophet()
model.fit(df)

future = model.make_future_dataframe(periods=30)  # 30日間の予測
forecast = model.predict(future)

fig = model.plot(forecast)
plt.xlabel('Date')
plt.ylabel('Stock Price')
plt.title('Random Stock Price Forecast')
plt.show()

import networkx as nx
import matplotlib.pyplot as plt

# グラフの作成
G = nx.DiGraph()

# タスクの追加
tasks = {
    'A': {'duration': 5},
    'B': {'duration': 3},
    'C': {'duration': 2},
    'D': {'duration': 4}
}

# タスク間の依存関係を追加
dependencies = [('A', 'B'), ('A', 'C'), ('B', 'D'), ('C', 'D')]

# グラフにタスクと依存関係を追加
for task, data in tasks.items():
    G.add_node(task, duration=data['duration'])

for dependency in dependencies:
    G.add_edge(dependency[0], dependency[1])

# トポロジカルソート
schedule = list(nx.topological_sort(G))

# タスクのスケジュール表示
print("スケジュール:")
for i, task in enumerate(schedule):
    print(f"Day {i + 1}: Task {task}")

# グラフの描画
pos = nx.spring_layout(G)
nx.draw(G, pos, with_labels=True, node_size=800, node_color='skyblue', font_weight='bold', font_size=12, edge_color='gray', arrowsize=20)
plt.title('Project Scheduling')
plt.show()

import networkx as nx
import matplotlib.pyplot as plt

G = nx.DiGraph()

tasks = {
    'A': {'duration': 5},
    'B': {'duration': 3},
    'C': {'duration': 2},
    'D': {'duration': 4}
}

dependencies = [('A', 'B'), ('A', 'C'), ('B', 'D'), ('C', 'D')]

for task, data in tasks.items():
    G.add_node(task, duration=data['duration'])

for dependency in dependencies:
    G.add_edge(dependency[0], dependency[1])

schedule = list(nx.topological_sort(G))

print("スケジュール:")
for i, task in enumerate(schedule):
    print(f"Day {i + 1}: Task {task}")

pos = nx.spring_layout(G)
nx.draw(G, pos, with_labels=True, node_size=800, node_color='skyblue', font_weight='bold', font_size=12, edge_color='gray', arrowsize=20)
plt.title('Project Scheduling')
plt.show()

import cvxpy as cp
import numpy as np
import matplotlib.pyplot as plt

# 変数の定義
x = cp.Variable()
y = cp.Variable()

# 最大化する式の定義
objective = cp.Maximize(cp.log(x) + cp.sqrt(y))

# 制約条件の定義
constraints = [
    x**2 + y**2 <= 50,
    2*x - y >= 5,
    x >= 1,
    y >= 1
]

# 最適化問題の定義
problem = cp.Problem(objective, constraints)

# 問題を解く
problem.solve()

# 解を表示
print("Optimal value:", problem.value)
print("Optimal x:", x.value)
print("Optimal y:", y.value)

# グラフで可視化
theta = np.linspace(0, 2*np.pi, 100)
circle_x = np.sqrt(50) * np.cos(theta)
circle_y = np.sqrt(50) * np.sin(theta)

plt.figure(figsize=(8, 6))
plt.plot(circle_x, circle_y, label='Constraint 1: x^2 + y^2 <= 50')
plt.fill_between(circle_x, circle_y, color='gray', alpha=0.2)
plt.plot([1, 15], [2 * x.value - 5, 2 * x.value - 5], label='Constraint 2: 2x - y >= 5')
plt.ylim([0, 10])
plt.xlim([0, 10])
plt.scatter(x.value, y.value, color='red', label='Optimal Point')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.title('Optimization with Constraints')
plt.grid(True)
plt.show()

import cvxpy as cp
import numpy as np
import matplotlib.pyplot as plt

# 変数の定義
x = cp.Variable()
y = cp.Variable()

# 最大化する式の定義
objective = cp.Maximize(cp.log(x) + cp.sqrt(y))

# 制約条件の定義
constraints = [
    x**2 + y**2 <= 50,
    2*x - y >= 5,
    x >= 1,
    y >= 1
]

# 最適化問題の定義
problem = cp.Problem(objective, constraints)

# 問題を解く
problem.solve()

# 解を表示
print("Optimal value:", problem.value)
print("Optimal x:", x.value)
print("Optimal y:", y.value)

# グラフで可視化
theta = np.linspace(0, 2*np.pi, 100)
circle_x = np.sqrt(50) * np.cos(theta)
circle_y = np.sqrt(50) * np.sin(theta)

plt.figure(figsize=(8, 6))
plt.plot(circle_x, circle_y, label='Constraint 1: x^2 + y^2 <= 50')
plt.fill_between(circle_x, circle_y, color='gray', alpha=0.2)
plt.plot([1, 15], [2 * x.value - 5, 2 * x.value - 5], label='Constraint 2: 2x - y >= 5')
plt.ylim([0, 10])
plt.xlim([0, 10])
plt.scatter(x.value, y.value, color='red', label='Optimal Point')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.title('Optimization with Constraints')
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA

# ダミーデータ生成
# 3つの国、4つの商品（リンゴ、バナナ、オレンジ、グレープ）の貿易データを生成します
np.random.seed(42)
countries = ['Country A', 'Country B', 'Country C']
num_products = 4
num_samples = 100

# ダミーの貿易データ生成
data = np.random.randint(0, 100, size=(num_samples, num_products * len(countries)))

# 主成分分析 (PCA) を用いて次元削減
pca = PCA(n_components=2)
transformed_data = pca.fit_transform(data)

# 可視化
plt.figure(figsize=(8, 6))
for i, country in enumerate(countries):
    indices = range(i * num_products, (i + 1) * num_products)
    plt.scatter(transformed_data[:, 0][indices], transformed_data[:, 1][indices], label=country)

plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.title('Trade Data Visualization')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA

np.random.seed(42)
countries = ['Country A', 'Country B', 'Country C']
num_products = 4
num_samples = 100
data = np.random.randint(0, 100, size=(num_samples, num_products * len(countries)))

pca = PCA(n_components=2)
transformed_data = pca.fit_transform(data)

plt.figure(figsize=(8, 6))
for i, country in enumerate(countries):
    indices = range(i * num_products, (i + 1) * num_products)
    plt.scatter(transformed_data[:, 0][indices], transformed_data[:, 1][indices], label=country)

plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.title('Trade Data Visualization')
plt.legend()
plt.grid(True)
plt.show()

import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# データの読み込み
url = "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data"
column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration', 'Model Year', 'Origin']
data = pd.read_csv(url, sep='\s+', names=column_names, na_values='?')

# 欠損値を削除
data = data.dropna()

# 特徴量とターゲットの選択
X = data[['Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration']]
y = data['MPG']

# モデルの訓練
model = LinearRegression()
model.fit(X, y)

# 予測と実際の値の比較
predictions = model.predict(X)

# グラフ化
plt.figure(figsize=(10, 6))
plt.scatter(y, predictions)
plt.xlabel('実際のMPG')
plt.ylabel('予測されたMPG')
plt.title('実際のMPGと予測されたMPGの比較')
plt.grid(True)
plt.show()

import numpy as np
from scipy.optimize import root
import matplotlib.pyplot as plt

# 解きたい方程式の関数
def equation(x):
    return x**3 + 2*x**2 - 5*x + 6

# 初期値を設定
initial_guess = 0

# 方程式の解を求める
solution = root(equation, initial_guess)

# 解を表示
print("解:", solution.x)

# グラフ化
x_vals = np.linspace(-5, 3, 1000)
y_vals = equation(x_vals)

plt.figure(figsize=(8, 6))
plt.plot(x_vals, y_vals, label='y = x^3 + 2x^2 - 5x + 6')
plt.scatter(solution.x, equation(solution.x), color='red', label='Solution', s=100)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Graph of the Equation')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
from scipy.optimize import root
import matplotlib.pyplot as plt

def equation(x):
    return x**3 + 2*x**2 - 5*x + 6

initial_guess = 0

solution = root(equation, initial_guess)

print("解:", solution.x)

x_vals = np.linspace(-5, 3, 1000)
y_vals = equation(x_vals)

plt.figure(figsize=(8, 6))
plt.plot(x_vals, y_vals, label='y = x^3 + 2x^2 - 5x + 6')
plt.scatter(solution.x, equation(solution.x), color='red', label='Solution', s=100)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Graph of the Equation')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt

# サンプルデータ生成
np.random.seed(0)
data = np.random.normal(loc=5, scale=2, size=1000)  # 平均=5, 標準偏差=2の正規分布からのサンプルデータ

# データの基本的な統計量計算
mean = np.mean(data)
std_dev = np.std(data)
median = np.median(data)

# 正規分布の確率密度関数の作成
x = np.linspace(np.min(data), np.max(data), 100)
pdf = norm.pdf(x, mean, std_dev)

# グラフ化
plt.figure(figsize=(10, 6))

# ヒストグラム
plt.hist(data, bins=30, density=True, alpha=0.5, label='Histogram')

# 正規分布の確率密度関数
plt.plot(x, pdf, 'r', label='Normal Distribution')

# 平均と中央値の縦線
plt.axvline(mean, color='green', linestyle='dashed', linewidth=2, label='Mean')
plt.axvline(median, color='orange', linestyle='dashed', linewidth=2, label='Median')

plt.title('Histogram and Normal Distribution of Data')
plt.xlabel('Values')
plt.ylabel('Density')
plt.legend()
plt.grid(True)
plt.show()

# 統計量の表示
print(f"平均: {mean}")
print(f"標準偏差: {std_dev}")
print(f"中央値: {median}")

import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt

np.random.seed(0)
data = np.random.normal(loc=5, scale=2, size=1000)

mean = np.mean(data)
std_dev = np.std(data)
median = np.median(data)

x = np.linspace(np.min(data), np.max(data), 100)
pdf = norm.pdf(x, mean, std_dev)

plt.figure(figsize=(10, 6))
plt.hist(data, bins=30, density=True, alpha=0.5, label='Histogram')
plt.plot(x, pdf, 'r', label='Normal Distribution')
plt.axvline(mean, color='green', linestyle='dashed', linewidth=2, label='Mean')
plt.axvline(median, color='orange', linestyle='dashed', linewidth=2, label='Median')
plt.title('Histogram and Normal Distribution of Data')
plt.xlabel('Values')
plt.ylabel('Density')
plt.legend()
plt.grid(True)
plt.show()

print(f"平均: {mean}")
print(f"標準偏差: {std_dev}")
print(f"中央値: {median}")

import cvxpy as cp
import numpy as np
import matplotlib.pyplot as plt

# データ生成
np.random.seed(42)
n = 5  # 資産の数
mean_returns = np.random.randn(n) / 100  # 平均収益率
cov_matrix = np.random.randn(n, n)
cov_matrix = np.dot(cov_matrix.T, cov_matrix) / 100  # 共分散行列

# ポートフォリオ最適化問題の定義
weights = cp.Variable(n)
expected_return = mean_returns.T @ weights
risk = cp.quad_form(weights, cov_matrix)
constraints = [
    cp.sum(weights) == 1,  # 資産の割合の合計は1
    weights >= 0  # 資産の割合は0以上
]
problem = cp.Problem(cp.Maximize(expected_return - 0.5 * risk), constraints)  # 目的関数

# 最適化
result = problem.solve()

# 結果を表示
print("最適化されたポートフォリオの割合:")
print(weights.value)
print("期待収益率:", expected_return.value)
print("リスク:", np.sqrt(risk.value))

# グラフ化
plt.figure(figsize=(8, 6))
plt.bar(range(n), weights.value, tick_label=[f"Asset {i+1}" for i in range(n)])
plt.xlabel('Assets')
plt.ylabel('Portfolio Weights')
plt.title('Optimized Portfolio Allocation')
plt.show()

import cvxpy as cp
import numpy as np
import matplotlib.pyplot as plt

np.random.seed(42)
n = 5  # 資産の数
mean_returns = np.random.randn(n) / 100  # 平均収益率
cov_matrix = np.random.randn(n, n)
cov_matrix = np.dot(cov_matrix.T, cov_matrix) / 100  # 共分散行列

weights = cp.Variable(n)
expected_return = mean_returns.T @ weights
risk = cp.quad_form(weights, cov_matrix)
constraints = [
    cp.sum(weights) == 1,  # 資産の割合の合計は1
    weights >= 0  # 資産の割合は0以上
]
problem = cp.Problem(cp.Maximize(expected_return - 0.5 * risk), constraints)  # 目的関数

result = problem.solve()

print("最適化されたポートフォリオの割合:")
print(weights.value)
print("期待収益率:", expected_return.value)
print("リスク:", np.sqrt(risk.value))

plt.figure(figsize=(8, 6))
plt.bar(range(n), weights.value, tick_label=[f"Asset {i+1}" for i in range(n)])
plt.xlabel('Assets')
plt.ylabel('Portfolio Weights')
plt.title('Optimized Portfolio Allocation')
plt.show()