How to convert python code to Matlab?
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Is there way to convert this python code to matlab code?
how can i convert python code to matlab???
this is the code that I want to convert:
import os
os.environ("KMP_DUPLICATE_LIB_OK") = "TRUE"; %%aggiungo una variabile ambiente
from sklearn.cluster import estimate_bandwidth
from sklearn import metrics
def estimate_bandwidth_meanshift(features, perc, quantile=0.5):
print('Start estimating bandwidth -------------------------------------')
bandwidth = estimate_bandwidth(features, quantile=quantile, n_samples = int(features.shape(0)*perc/100))
print('End estimating bandwidth ---------------------------------------')
return bandwidth
def getNMI(prediction, gt):
return metrics.normalized_mutual_info_score(gt, prediction)
def getARI(prediction, gt):
return metrics.adjusted_rand_score(gt, prediction)
3 个评论
Rik
2022-11-9
There is no automated conversion possible.
You have two options:
- Look up the meaning of each function and implement the working of the code in Matlab.
- Run the python code from within Matlab.
Which did you try?
filippo gistri
2022-11-11
Rik
2022-11-11
I don't work with the Python link enough to understand this error without seeing the code you tried.
采纳的回答
Aditya Jha
2022-11-16
1 个投票
Hi!
There is no direct way to convert python code to MATLAB.
There are two approaches:
- You have to directly write the code from scratch.
- You can use MATLB API to call python script in MATLAB
- https://www.mathworks.com/help/matlab/call-python-libraries.html
- As of MATLAB 8.4(R2014b) you can call Python directly from MATLAB
Refer to the following MATLAB answers post with similar issue:
更多回答（4 个）
rushabh rupani
2023-1-28
0 个投票
Converting Python code to Matlab can be a complex process, as the two languages have different syntax and libraries.
Lekkalapudi Chandini
2023-4-19
% Plot the displacement versus time
plot(t, x(:, 1))
xlabel('Time (s)')
ylabel('Displacement (m)')
title('Displacement of Mechanical System')
Anoy Chowdhury
2023-7-20
Only some commands like
py.list({'This','is a','list'})
are recommendable to use from python. In the other hand, you can create a full funcion in python script / function and call it with:
pyrun('my_python_callback')
However, trying to "convert" a python script line by line to MATLAB is pointless. To give you a context, is equivalent to try to convert to python some script which includes:
%...
sys = armax(tt2,[na nb nc nk])
%...
Since armax belongs to a toolbox (System Identification) it would be extremely complicated and unpractical.
If you want to use a python library as sklearn.cluster, stay in python, the only reason to import to MATLAB should be importing a python environment to interact with an MATLAB , for example an MATLAB AI agent:
classdef mountain_car_1 < rl.env.MATLABEnvironment
properties
open_env = py.gym.make('MountainCar-v0'); %% IMPORT PYTHON OPEN AI ENVIRONMENT
end
methods
function this = mountain_car_1()
ObservationInfo = rlNumericSpec([2 1]);
ObservationInfo.Name = 'MountainCar Descreet';
ObservationInfo.Description = 'Position, Velocity';
ActionInfo = rlFiniteSetSpec([0 1 2]);
ActionInfo.Name = 'Acceleration direction';
this = this@rl.env.MATLABEnvironment(ObservationInfo,ActionInfo);
end
function [Observation,Reward,IsDone,LoggedSignals] = step(this,Action)
result = cell(this.open_env.step(int16(Action)));
Observation = double(result{1})';
Reward = double(result{2});
IsDone = double(result{3});
LoggedSignals = [];
if (Observation(1)>=0.4)
Reward = 0;
IsDone = 1;
end
end
function InitialObservation = reset(this)
result = this.open_env.reset();
InitialObservation = double(result)';
end
end
end
In the previous example, if you had installed Python 3.10, and OpenAIGym, you can import the desired environment to MATLAB and interact both with the used and a potencial AI Bot.
Yerisn
2024-6-19
0 个投票
import numpy as np
# Paso 1: Definir parámetros geométricos y mecánicos
E = 200000 # Módulo elástico en MPa
alpha = 12e-6 # Coeficiente de dilatación térmica en 1/°C
delta_T = -20 # Diferencia de temperatura por enfriamiento en °C
A1 = 6000 # Área de la barra 1 en mm^2
A2 = 5000 # Área de la barra 2 en mm^2
A3 = 4000 # Área de la barra 3 en mm^2
L1 = 6000 # Longitud de la barra 1 en mm
L2 = 8000 # Longitud de la barra 2 en mm
L3 = 5000 # Longitud de la barra 3 en mm
angles = [-np.pi/2, 5 * np.pi / 6, np.pi / 4] # Ángulos en radianes
# Paso 2: Definir la tabla de conectividades
connectivity = np.array([[1, 2], [1, 4], [1, 3]]) # Nodos
dof_connectivity = np.array([[0, 1, 2, 3], [0, 1, 6, 7], [0, 1, 4, 5]]) # Grados de libertad
print("Tabla de conectividades")
print(connectivity.reshape((3, 2)))
print(dof_connectivity.reshape((3, 4)))
# Paso 3: Definir las matrices de rigidez elementales referidas a los sistemas de referencia locales
k1_local = (E * A1 / L1) * np.array([[1, -1], [-1, 1]])
k2_local = (E * A2 / L2) * np.array([[1, -1], [-1, 1]])
k3_local = (E * A3 / L3) * np.array([[1, -1], [-1, 1]])
print("Matrices de rigidez elementales referidas a los sistemas de referencia locales")
print(k1_local)
print(k2_local)
print(k3_local)
# Paso 4: Definir los vectores de cargas elementales (por temperatura y rigidez) referida a los sistemas de referencia locales
f_T1 = np.array([-alpha * delta_T * E * A1, alpha * delta_T * E * A1])
f_T2 = np.array([-alpha * delta_T * E * A2, alpha * delta_T * E * A2])
f_T3 = np.array([-alpha * delta_T * E * A3, alpha * delta_T * E * A3])
print("Vectores de cargas elementales")
print(f_T1)
print(f_T2)
print(f_T3)
# Paso 5: Definir las matrices de rotación de cada barra
def rotation_matrix(angle):
c = np.cos(angle)
s = np.sin(angle)
return np.array([[c, s, 0, 0], [0, 0, c, s]])
R1 = rotation_matrix(angles[0]) # Barra 1 (ángulo de -π/2 radianes)
R2 = rotation_matrix(angles[1]) # Barra 2 (ángulo de 5π/6 radianes)
R3 = rotation_matrix(angles[2]) # Barra 3 (ángulo de π/4 radianes)
print("Matrices de rotación de cada barra")
print(R1)
print(R2)
print(R3)
# Paso 6: Transformar las matrices de rigidez del sistema de referencia local al sistema de referencia global
k1_global = R1.T @ k1_local @ R1
k2_global = R2.T @ k2_local @ R2
k3_global = R3.T @ k3_local @ R3
print("Transformación de las matrices de rigidez del sistema de referencia local al sistema de referencia global")
print(k1_global)
print(k2_global)
print(k3_global)
# Paso 7: Transformar los vectores de carga del sistema de referencia local al sistema de referencia global
F_T1_global = R1.T @ f_T1
F_T2_global = R2.T @ f_T2
F_T3_global = R3.T @ f_T3
print("Transformación de los vectores de carga del sistema de referencia local al sistema de referencia global")
print(F_T1_global)
print(F_T2_global)
print(F_T3_global)
# Paso 8: Ensamblar la matriz de rigidez global (8x8)
K_global = np.zeros((8, 8))
# Contribución de la barra 1 (nodo 1 a nodo 2)
K_global[np.ix_([0, 1, 2, 3], [0, 1, 2, 3])] += k1_global
# Contribución de la barra 2 (nodo 1 a nodo 4)
K_global[np.ix_([0, 1, 6, 7], [0, 1, 6, 7])] += k2_global
# Contribución de la barra 3 (nodo 1 a nodo 3)
K_global[np.ix_([0, 1, 4, 5], [0, 1, 4, 5])] += k3_global
print("Matriz de rigidez global")
print(K_global)
# Paso 9: Ensamblar el vector de carga global
F_global = np.zeros(8)
F_global[0:2] += F_T1_global[:2]
F_global[0:2] += F_T2_global[:2]
F_global[0:2] += F_T3_global[:2]
print("Vector de carga global")
print(F_global)
# Condiciones de contorno: nodos 2, 3 y 4 son empotrados (desplazamientos = 0)
fixed_dofs = [2, 3, 4, 5, 6, 7]
free_dofs = [0, 1]
# Reducción de la matriz de rigidez y el vector de fuerzas
K_reduced = K_global[np.ix_(free_dofs, free_dofs)]
F_reduced = F_global[free_dofs]
# Paso 10: Resolver las incógnitas
# a) Desplazamiento del nodo central 1
displacements = np.linalg.solve(K_reduced, F_reduced)
# Vector de desplazamientos completo
displacement_vector = np.zeros(8)
displacement_vector[free_dofs] = displacements
# b) Calcular las reacciones de vínculo en los empotramientos
reactions = K_global @ displacement_vector - F_global
# c) Calcular deformaciones y tensiones en cada barra
deformation_1 = (displacement_vector[1] - displacement_vector[0]) / L1
deformation_2 = (displacement_vector[7] - displacement_vector[6]) / L2
deformation_3 = (displacement_vector[5] - displacement_vector[4]) / L3
stress_1 = E * deformation_1
stress_2 = E * deformation_2
stress_3 = E * deformation_3
# Resultados en formato de matrices
print("Desplazamiento del nodo central 1 (x, y):")
print(displacements.reshape((2, 1)))
print("Reacciones de vínculo en los empotramientos:")
print(reactions.reshape((8, 1)))
print("Deformaciones en cada barra (1, 2, 3):")
print(np.array([deformation_1, deformation_2, deformation_3]).reshape((3, 1)))
print("Tensiones en cada barra (1, 2, 3):")
print(np.array([stress_1, stress_2, stress_3]).reshape((3, 1)))
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