高校数学と機械学習

import numpy as np
import matplotlib.pyplot as plt

# Assign values to A and B
A = 10
B = 24

# Define the range of x values
x_values = np.linspace(-10, 20, 400)

# Compute corresponding y values for each equation
# From equation 1: y = A - x
y1 = A - x_values

# From equation 2: y = (B - 2x) / 4
y2 = (B - 2 * x_values) / 4

# Plot both lines
plt.plot(x_values, y1, label=f'x + y = {A}')
plt.plot(x_values, y2, label=f'2x + 4y = {B}')

# Solve for the intersection point
# Coefficient matrix
coefficients = np.array([[1, 1],
                         [2, 4]])

# Constants vector
constants = np.array([A, B])

# Use numpy's linear algebra solver
solution = np.linalg.solve(coefficients, constants)
x_intersect, y_intersect = solution

# Plot the intersection point
plt.plot(x_intersect, y_intersect, 'ro', label='Intersection Point')

# Calculate and plot the slope indicators
# Slope of the first line: -1
plt.arrow(0, A, 1, -1, head_width=0.5, head_length=1, fc='blue', ec='blue', label='Slope -1')

# Slope of the second line: -1/2
plt.arrow(0, B/4, 2, -1, head_width=0.5, head_length=1, fc='green', ec='green', label='Slope -1/2')

# Configure the plot
plt.xlabel('x')
plt.ylabel('y')
plt.title('Plot of Two Lines with Slopes Indicated')
plt.legend()
plt.grid(True)
plt.axis('equal')  # To ensure equal scaling

# Display the plot
plt.show()

# Print the solution
print(f'The solution to the system is x = {x_intersect}, y = {y_intersect}')

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.neural_network import MLPRegressor
from scipy.optimize import minimize
from sklearn.preprocessing import StandardScaler

# Define the coefficients and constants of the system
A = 10
B = 24

# Create the dataset
X = np.array([[1, 1], [2, 4]])  # Coefficients
y = np.array([A, B])  # Constants

# Standardize the data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Linear Regression
lin_reg = LinearRegression()
lin_reg.fit(X_scaled, y)
lr_solution = lin_reg.coef_

# Neural Network
mlp = MLPRegressor(hidden_layer_sizes=(10, 10), max_iter=20000, solver='adam', activation='relu')
mlp.fit(X_scaled, y)
nn_solution = mlp.coefs_[0].flatten()

# Optimization using Gradient Descent
def loss_function(w):
    return np.sum((np.dot(X, w) - y) ** 2)

initial_guess = np.zeros(X.shape[1])
opt_result = minimize(loss_function, initial_guess, method='BFGS')
opt_solution = opt_result.x

# Display the solutions
print(f"Linear Regression Solution: x = {lr_solution[0]}, y = {lr_solution[1]}")
print(f"Neural Network Solution: x = {nn_solution[0]}, y = {nn_solution[1]}")
print(f"Optimization Solution: x = {opt_solution[0]}, y = {opt_solution[1]}")

# Plotting the original lines for reference
x_values = np.linspace(-10, 20, 400)
y1 = A - x_values
y2 = (B - 2 * x_values) / 4

plt.plot(x_values, y1, label=f'x + y = {A}')
plt.plot(x_values, y2, label=f'2x + 4y = {B}')

# Plot the solutions
plt.plot(lr_solution[0], lr_solution[1], 'bo', label='Linear Regression Solution')
plt.plot(nn_solution[0], nn_solution[1], 'go', label='Neural Network Solution')
plt.plot(opt_solution[0], opt_solution[1], 'ro', label='Optimization Solution')

# Configure the plot
plt.xlabel('x')
plt.ylabel('y')
plt.title('Solutions using Linear Regression, Neural Network, and Optimization')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.neural_network import MLPRegressor
from scipy.optimize import minimize
from sklearn.preprocessing import StandardScaler

# Define the coefficients and constants of the system
A = 10
B = 24

# Create the dataset
X = np.array([[1, 1], [2, 4]])  # Coefficients
y = np.array([A, B])  # Constants

# Standardize the data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Linear Regression
lin_reg = LinearRegression()
lin_reg.fit(X_scaled, y)
lr_solution = lin_reg.coef_

# Neural Network
mlp = MLPRegressor(hidden_layer_sizes=(10, 10), max_iter=20000, solver='adam', activation='relu')
mlp.fit(X_scaled, y)
nn_solution = mlp.coefs_[0].flatten()

# Optimization using Gradient Descent
def loss_function(w):
    return np.sum((np.dot(X, w) - y) ** 2)

initial_guess = np.zeros(X.shape[1])
opt_result = minimize(loss_function, initial_guess, method='BFGS')
opt_solution = opt_result.x

# Display the solutions
print(f"Linear Regression Solution: x = {lr_solution[0]}, y = {lr_solution[1]}")
print(f"Neural Network Solution: x = {nn_solution[0]}, y = {nn_solution[1]}")
print(f"Optimization Solution: x = {opt_solution[0]}, y = {opt_solution[1]}")

# Plotting the original lines for reference
x_values = np.linspace(-10, 20, 400)
y1 = A - x_values
y2 = (B - 2 * x_values) / 4

plt.plot(x_values, y1, label=f'x + y = {A}')
plt.plot(x_values, y2, label=f'2x + 4y = {B}')

# Plot the solutions
plt.plot(lr_solution[0], lr_solution[1], 'bo', label='Linear Regression Solution')
plt.plot(nn_solution[0], nn_solution[1], 'go', label='Neural Network Solution')
plt.plot(opt_solution[0], opt_solution[1], 'ro', label='Optimization Solution')

# Configure the plot
plt.xlabel('x')
plt.ylabel('y')
plt.title('Solutions using Linear Regression, Neural Network, and Optimization')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# データの生成
np.random.seed(0)  # 再現性のためのシード設定
time = np.linspace(0, 10, 50)  # 0秒から10秒までの50ポイント
velocity = 2  # 速度 (m/s)
distance = velocity * time + np.random.normal(scale=0.5, size=time.shape)  # 移動距離にノイズを追加

# 最小二乗法による直線フィット
# 行列Aの作成（時間と定数項のマトリックス）
A = np.vstack([time, np.ones(len(time))]).T

# 最小二乗法によるパラメータの計算
m, b = np.linalg.lstsq(A, distance, rcond=None)[0]

# フィットした直線の計算
fit_line = m * time + b

# プロット
plt.figure(figsize=(8, 6))
plt.scatter(time, distance, color='red', label='Noisy Data Points')  # データポイント
plt.plot(time, fit_line, color='blue', label=f'Fitted Line: y = {m:.2f}x + {b:.2f}')  # フィットした直線
plt.xlabel('Time (seconds)')
plt.ylabel('Distance (meters)')
plt.title('Distance vs. Time with Least Squares Fit')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# データの生成
np.random.seed(0)  # 再現性のためのシード設定
time = np.linspace(0, 10, 50)  # 0秒から10秒までの50ポイント
g = 9.8  # 重力加速度 (m/s^2)
v0 = 0  # 初速度 (m/s)
y0 = 0  # 初期位置 (m)

# 自由落下のデータ生成 (ノイズを追加)
distance = -0.5 * g * time**2 + v0 * time + y0 + np.random.normal(scale=0.5, size=time.shape)

# 行列形式でデータを準備
X = np.vstack([time**2, time, np.ones(len(time))]).T  # 二次項、一次項、定数項

# 最小二乗法によるパラメータの計算
coefficients = np.linalg.lstsq(X, distance, rcond=None)[0]
a, b, c = coefficients

# フィットした二次関数の計算
fit_line = a * time**2 + b * time + c

# プロット
plt.figure(figsize=(8, 6))
plt.scatter(time, distance, color='red', label='Noisy Data Points')  # データポイント
plt.plot(time, fit_line, color='blue', label=f'Fitted Quadratic: y = {a:.2f}t^2 + {b:.2f}t + {c:.2f}')  # フィットした二次関数
plt.xlabel('Time (seconds)')
plt.ylabel('Distance (meters)')
plt.title('Distance vs. Time with Quadratic Fit')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Initial conditions
x0 = 0    # Initial position (m)
v0 = 5    # Initial velocity (m/s)
a = 2     # Acceleration (m/s^2)

# Time range
t = np.linspace(0, 10, 100)  # Generate 100 points from 0 to 10 seconds

# Calculate position
x = x0 + v0 * t + 0.5 * a * t**2

# Plot
plt.figure(figsize=(8, 6))
plt.plot(t, x, label='Position x(t)')
plt.xlabel('Time (seconds)')
plt.ylabel('Position (meters)')
plt.title('Position vs Time for Uniformly Accelerated Motion')
plt.legend()
plt.grid(True)
plt.show()

from scipy.optimize import minimize

# 定義する目的関数
def objective(vars):
    x, y = vars
    return - (x + y)  # 最大化問題にするために符号を反転

# 制約条件
def constraint(vars):
    x, y = vars
    return 1 - (x**2 + y**2)  # x^2 + y^2 <= 1 の形式

# 初期値
initial_guess = [0, 0]

# 制約の設定
con = {'type': 'ineq', 'fun': constraint}

# 最適化の実行
result = minimize(objective, initial_guess, constraints=con)

# 結果の表示
x_opt, y_opt = result.x
f_opt = -result.fun

# グラフのプロット
theta = np.linspace(0, 2*np.pi, 100)
x_circle = np.cos(theta)
y_circle = np.sin(theta)

plt.figure(figsize=(8, 6))
plt.plot(x_circle, y_circle, label='Constraint: x^2 + y^2 = 1')
plt.scatter(x_opt, y_opt, color='red', label=f'Optimal point at ({x_opt:.2f}, {y_opt:.2f}), f(x, y)={f_opt:.2f}')
plt.xlabel('x')
plt.ylabel('y')
plt.title('Optimization with Constraint')
plt.legend()
plt.grid(True)
plt.axis('equal')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Define the quadratic function
def f(x):
    a = 1
    b = -4
    c = 4
    return a * x**2 + b * x + c

# Define the cost function for gradient descent
def cost_function(x):
    return x**2 - 4*x + 4

# Gradient of the cost function
def gradient(x):
    return 2*x - 4

# Gradient Descent Algorithm
def gradient_descent(starting_point, learning_rate, num_iterations):
    x = starting_point
    history = [x]
    for _ in range(num_iterations):
        x = x - learning_rate * gradient(x)
        history.append(x)
    return x, history

# Parameters for the quadratic function
a = 1
b = -4
c = 4

# Completing the square
vertex_x = -b / (2 * a)
vertex_y = f(vertex_x)

# Parameters for gradient descent
starting_point = 0.0
learning_rate = 0.1
num_iterations = 50

# Run Gradient Descent
optimal_x, history = gradient_descent(starting_point, learning_rate, num_iterations)
optimal_cost = cost_function(optimal_x)

# Create x values for plotting
x = np.linspace(-1, 5, 100)
y = f(x)

# Plot the quadratic function, its minimum, and gradient descent path
plt.figure(figsize=(12, 6))

# Plot the quadratic function
plt.plot(x, y, label='f(x) = x^2 - 4x + 4', color='blue')

# Plot the minimum point from completing the square
plt.scatter(vertex_x, vertex_y, color='red', zorder=5, label=f'Minimum at x={vertex_x:.2f}, f(x)={vertex_y:.2f}')

# Plot the gradient descent path
history_x = np.array(history)
history_y = cost_function(history_x)
plt.scatter(history_x, history_y, color='green', zorder=5, label='Gradient Descent Path')

plt.xlabel('x')
plt.ylabel('f(x)')
plt.title('Quadratic Function, Minimum Value, and Gradient Descent Path')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import minimize

# Objective function
def objective(vars):
    x, y = vars
    return x**2 + y**2

# Constraint function
def constraint(vars):
    x, y = vars
    return x + 2*y - 5

# Initial guess
initial_guess = [0, 0]

# Define constraints and bounds
con = {'type': 'eq', 'fun': constraint}

# Perform the optimization
result = minimize(objective, initial_guess, constraints=con)

# Extract the optimal values
x_opt, y_opt = result.x
min_value = result.fun

print(f"Optimal values: x = {x_opt:.2f}, y = {y_opt:.2f}")
print(f"Minimum value of x^2 + y^2: {min_value:.2f}")

# Create a grid of points
x = np.linspace(-1, 6, 400)
y = np.linspace(-1, 3, 400)
X, Y = np.meshgrid(x, y)
Z = X**2 + Y**2

# Plot the contours of x^2 + y^2
plt.figure(figsize=(8, 6))
cp = plt.contour(X, Y, Z, levels=20, cmap='viridis')
plt.colorbar(cp, label='Objective Function: $x^2 + y^2$')

# Plot the constraint line x + 2y = 5
y_line = (5 - x) / 2
plt.plot(x, y_line, 'r--', label='$x + 2y = 5$')

# Plot the optimal point
plt.plot(x_opt, y_opt, 'bo', label='Optimal Point')

plt.xlabel('$x$')
plt.ylabel('$y$')
plt.title('Contour plot of $x^2 + y^2$ and constraint $x + 2y = 5$')
plt.legend()
plt.grid(True)
plt.show()

# 3D plot
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')

# Plot the surface
ax.plot_surface(X, Y, Z, cmap='viridis', alpha=0.8)

# Plot the constraint line in 3D
x_line = np.linspace(-1, 6, 400)
y_line = (5 - x_line) / 2
X_line, Y_line = np.meshgrid(x_line, y_line)
Z_line = X_line**2 + Y_line**2
ax.plot_surface(X_line, Y_line, Z_line, color='r', alpha=0.5, linewidth=0, antialiased=False)

# Plot the optimal point
ax.scatter(x_opt, y_opt, min_value, color='b', s=100, label='Optimal Point')

ax.set_xlabel('$x$')
ax.set_ylabel('$y$')
ax.set_zlabel('$x^2 + y^2$')
ax.set_title('3D plot of $x^2 + y^2$ and constraint $x + 2y = 5$')
ax.legend()

plt.show()

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

# Objective function
def objective(vars):
    x, y = vars
    return x**2 + y**2

# Constraint function
def constraint(vars):
    x, y = vars
    return x + 2*y - 5

# Initial guess
initial_guess = [0, 0]

# Define constraints and bounds
con = {'type': 'eq', 'fun': constraint}

# Perform the optimization using SLSQP method
result = minimize(objective, initial_guess, constraints=con, method='SLSQP')

# Extract the optimal values
x_opt, y_opt = result.x
min_value = result.fun

print(f"Optimal values: x = {x_opt:.2f}, y = {y_opt:.2f}")
print(f"Minimum value of x^2 + y^2: {min_value:.2f}")

# Create a grid of points
x = np.linspace(-1, 6, 400)
y = np.linspace(-1, 3, 400)
X, Y = np.meshgrid(x, y)
Z = X**2 + Y**2

# Plot the contours of x^2 + y^2
plt.figure(figsize=(8, 6))
cp = plt.contour(X, Y, Z, levels=20, cmap='viridis')
plt.colorbar(cp, label='Objective Function: $x^2 + y^2$')

# Plot the constraint line x + 2y = 5
y_line = (5 - x) / 2
plt.plot(x, y_line, 'r--', label='$x + 2y = 5$')

# Plot the optimal point
plt.plot(x_opt, y_opt, 'bo', label='Optimal Point')

plt.xlabel('$x$')
plt.ylabel('$y$')
plt.title('Contour plot of $x^2 + y^2$ and constraint $x + 2y = 5$')
plt.legend()
plt.grid(True)
plt.show()

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

# Objective function
def objective(vars):
    x, y = vars
    return x**2 + y**2

# Constraint function
def constraint(vars):
    x, y = vars
    return x + 2*y - 5

# Initial guess
initial_guess = [0, 0]

# Define constraints and bounds
con = {'type': 'eq', 'fun': constraint}

# Perform the optimization using improved settings
result = minimize(objective, initial_guess, constraints=con, method='SLSQP', options={'disp': True, 'maxiter': 1000})

# Extract the optimal values
x_opt, y_opt = result.x
min_value = result.fun

print(f"Optimal values with improved settings: x = {x_opt:.2f}, y = {y_opt:.2f}")
print(f"Minimum value of x^2 + y^2 with improved settings: {min_value:.2f}")

# Create a grid of points
x = np.linspace(-1, 6, 400)
y = np.linspace(-1, 3, 400)
X, Y = np.meshgrid(x, y)
Z = X**2 + Y**2

# Plot the contours of x^2 + y^2
plt.figure(figsize=(8, 6))
cp = plt.contour(X, Y, Z, levels=20, cmap='viridis')
plt.colorbar(cp, label='Objective Function: $x^2 + y^2$')

# Plot the constraint line x + 2y = 5
y_line = (5 - x) / 2
plt.plot(x, y_line, 'r--', label='$x + 2y = 5$')

# Plot the optimal point with improved settings
plt.plot(x_opt, y_opt, 'bo', label='Optimal Point with Improved Settings')

plt.xlabel('$x$')
plt.ylabel('$y$')
plt.title('Contour plot of $x^2 + y^2$ and constraint $x + 2y = 5$ with Improved Settings')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.cluster import KMeans
from sklearn.datasets import make_classification
from sklearn.preprocessing import StandardScaler

# 点と直線の距離を計算する関数
def point_line_distance(x0, y0, a, b, c):
    return np.abs(a * x0 + b * y0 + c) / np.sqrt(a**2 + b**2)

# データ生成
X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=42)

# データの標準化
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# SVMで分類
clf = svm.SVC(kernel='linear', C=1.0)
clf.fit(X_scaled, y)

# 直線の係数
w = clf.coef_[0]
b = clf.intercept_[0]
a, b = -w[0], -w[1]
c = -b

# 各点から直線までの距離を計算
distances = np.array([point_line_distance(x, y, a, b, c) for x, y in X_scaled])

# K-meansクラスタリング
kmeans = KMeans(n_clusters=2, random_state=42)
kmeans.fit(X_scaled)
labels = kmeans.labels_

# プロット
plt.figure(figsize=(12, 6))

# SVMの決定境界
xx, yy = np.meshgrid(np.linspace(X_scaled[:, 0].min(), X_scaled[:, 0].max(), 500),
                     np.linspace(X_scaled[:, 1].min(), X_scaled[:, 1].max(), 500))
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

plt.contour(xx, yy, Z, colors='k', levels=[-1, 0, 1], alpha=0.5, linestyles=['--', '-', '--'])

# K-meansクラスタリングの結果
plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=labels, s=50, cmap='viridis')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], c='red', s=200, alpha=0.75, marker='X')

plt.title('SVM and K-means Clustering')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Sample data
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])

# Calculate means
x_mean = np.mean(x)
y_mean = np.mean(y)

# Calculate covariance and variance
S_xy = np.mean((x - x_mean) * (y - y_mean))
S_x2 = np.mean((x - x_mean) ** 2)

# Calculate coefficients a and intercept b
a = S_xy / S_x2
b = y_mean - a * x_mean

# Linear function
def linear_function(x):
    return a * x + b

# Calculate residuals
predicted_y = linear_function(x)
residuals = y - predicted_y

# Plot data points, the fitted line, and residuals
plt.figure(figsize=(10, 6))

# Plot data points
plt.scatter(x, y, color='blue', label='Data Points', zorder=5)

# Plot fitted line
plt.plot(x, predicted_y, color='red', label='Fitted Line', linewidth=2, zorder=4)

# Plot residuals
for i in range(len(x)):
    plt.plot([x[i], x[i]], [y[i], predicted_y[i]], color='green', linestyle='--', zorder=3)

# Graph details
plt.xlabel('x')
plt.ylabel('y')
plt.title('Least Squares Fitting with Residuals')
plt.legend()
plt.grid(True)
plt.show()

# Display results
print(f"Mean of x: {x_mean}")
print(f"Mean of y: {y_mean}")
print(f"Covariance S_xy: {S_xy}")
print(f"Variance S_x2: {S_x2}")
print(f"Coefficient a: {a}")
print(f"Intercept b: {b}")
print(f"Residuals: {residuals}")

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import classification_report

# Generate synthetic dataset
X, y = make_classification(n_samples=1000, n_features=10, n_classes=2, random_state=42)

# Split dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Standardize features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train SVM model
model = SVC(kernel='linear')
model.fit(X_train_scaled, y_train)

# Predict and evaluate
y_pred = model.predict(X_test_scaled)
print(classification_report(y_test, y_pred))

import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# データの生成
np.random.seed(0)
n_samples = 1000
X = np.linspace(0, 4 * np.pi, n_samples)
y = np.sin(X) > 0  # サイン波の正の部分をクラス1、負の部分をクラス0としてラベル付け

# サイン波とコサイン波を生成
X_data = np.vstack((np.sin(X), np.cos(X))).T
y_data = y.astype(int)

# データをトレーニングセットとテストセットに分割
X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.3, random_state=0)

# ロジスティック回帰モデルの訓練
model = LogisticRegression()
model.fit(X_train, y_train)

# テストセットで予測
y_pred = model.predict(X_test)

# 精度を表示
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')

# データと決定境界をプロット
plt.figure(figsize=(10, 6))

# プロットする領域を設定
x_min, x_max = X_data[:, 0].min() - 1, X_data[:, 0].max() + 1
y_min, y_max = X_data[:, 1].min() - 1, X_data[:, 1].max() + 1
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100), np.linspace(y_min, y_max, 100))
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

# 決定境界をプロット
plt.contourf(xx, yy, Z, alpha=0.3, cmap='coolwarm')
plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, edgecolor='k', cmap='coolwarm', marker='o')
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, edgecolor='k', cmap='coolwarm', marker='x')
plt.title('Logistic Regression Decision Boundary')
plt.xlabel('Sin(x)')
plt.ylabel('Cos(x)')
plt.colorbar()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from numpy.fft import fft, fftfreq

# サンプリングパラメータ
sampling_rate = 1000  # サンプリングレート (Hz)
T = 1.0 / sampling_rate  # サンプリング間隔 (秒)
L = 1.0  # 信号の長さ (秒)
N = int(L * sampling_rate)  # 信号のサンプル数

# 時間軸の生成
t = np.linspace(0.0, L, N, endpoint=False)

# 増加する指数関数の信号生成
A = 1.0  # 振幅
B = 5.0  # 成長率
signal = A * np.exp(B * t)

# FFTの計算
yf = fft(signal)
xf = fftfreq(N, T)[:N//2]

# プロット
plt.figure(figsize=(12, 6))

# 増加する指数関数の信号のプロット
plt.subplot(1, 2, 1)
plt.plot(t, signal)
plt.title('Exponential Signal')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')

# FFT結果のプロット
plt.subplot(1, 2, 2)
plt.plot(xf, np.abs(yf[:N//2]), label='Magnitude')
plt.title('FFT of Exponential Signal')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Magnitude')
plt.legend()

plt.tight_layout()
plt.show()