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Browse files- app (4).py +70 -0
- requirements.txt +2 -0
app (4).py
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import streamlit as st
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import numpy as np
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import matplotlib.pyplot as plt
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import optuna
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st.title("Optimization tool")
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func_user = st.selectbox("Choose a function:", ("cuadratic", "sine", "gaussian"))
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# ("sin(x/10)", "(x-2)^2", "exp-(x-4)^2")
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def func_cuadratic(x):
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return (x - 2) ** 2
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def func_sine(x):
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return np.sin(x / 10)
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def func_gauss(x):
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return np.exp(-((x - 4) ** 2))
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if func_user == "cuadratic":
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st.latex(r"(x - 2)^2")
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func_to_use = func_cuadratic
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elif func_user == "sine":
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st.latex(r"sin({ x \over 10 })")
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func_to_use = func_sine
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else:
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st.latex(r"e^{-(x-4)^2}")
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func_to_use = func_gauss
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opt_user = st.selectbox("Choose the optimization direction:", ("minimize", "maximize"))
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x_low = st.number_input("Please, give me the lower bound:", value=-10)
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x_upp = st.number_input("Please, give me the upper bound:", value=10)
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def objective(trial):
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x = trial.suggest_float("x", x_low, x_upp)
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return func_to_use(x)
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study = optuna.create_study(direction=opt_user)
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study.optimize(objective, n_trials=500)
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x_opt = study.best_params["x"]
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y_opt = func_to_use(x_opt)
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st.write(f"The critical point found is ({x_opt:,.4f}, {y_opt:,.4f}).")
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x_to_use = np.linspace(x_low, x_upp)
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plt.title("Plot of critical point within interval given by user")
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plt.xlabel("x axis")
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plt.ylabel("y axis")
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plt.plot(x_to_use, func_to_use(x_to_use))
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plt.scatter(x_opt, func_to_use(x_opt), c="red")
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plt.annotate(
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f"({x_opt:,.2f}, {y_opt:,.2f})",
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(x_opt, y_opt),
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textcoords="offset points",
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ha="center",
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)
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st.pyplot(plt)
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requirements.txt
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optuna
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plotly
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