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Commit f0227492 authored by David Preiss's avatar David Preiss
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prep for final

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final/numpy_stl_experiment.py 0 → 100644
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View file @ f0227492
import os
import numpy as np
from stl import mesh
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
def load_stl_files():
stl_files = [file for file in os.listdir() if file.endswith('.stl')]
meshes = []
for file in stl_files:
meshes.append(mesh.Mesh.from_file(file))
return meshes
def main():
# Load STL files
meshes = load_stl_files()
# Create a 3D plot
figure = plt.figure()
axes = mplot3d.Axes3D(figure)
# Plot the meshes
for mesh_data in meshes:
axes.add_collection3d(mplot3d.art3d.Poly3DCollection(mesh_data.vectors))
# Set plot parameters
axes.auto_scale_xyz([-100, 100], [-100, 100], [-100, 100])
plt.axis('off')
# Show the plot
plt.show()
if __name__ == '__main__':
main()
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final/rotor.SLDPRT 0 → 100644
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ISO-10303-21;
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'1' );
FILE_NAME ('rotor.STEP',
'2023-05-14T01:01:21',
( '' ),
( '' ),
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'SolidWorks 2021',
'' );
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import steputils
# Get the path to the .step file
file_path = "rotor.stp"
# Create a STEP reader
reader = steputils.Reader()
# Read the file
reader.read_file(file_path)
# Get the shape from the reader
shape = reader.shape()
# Create a visualization of the shape
visualizer = steputils.Visualizer()
# Display the visualization
visualizer.display(shape)
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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from pylfsr import LFSR
import torch
# print(torch.cuda.is_available())
from torch import nn
from torch.optim import Adam
from torch import Tensor
'''
17_1:
Train a multi-layer perceptron to predict the output of an order 10 maximal length linear feedback shift register
from the preceding 10 values, then generate new data by feeding the output back to the input and compare the generated and correct sequences.
This XOR example was a helpful reference:
https://www.youtube.com/watch?v=3I_66lyFOqI
'''
def create_data(state_0 = [1,0,0,0,0,0,0,0,0,0]):
L = LFSR(initstate = state_0, verbose = False)
x = []
y = []
for i in range(2**10):
x.append(L.state)
y.append([L.outbit])
L.next()
x = np.array(x, dtype = np.float32)
y = np.array(y, dtype = np.float32)
# override the first outbit which is -1
y[0] = [0]
return x, y
class LFSRModel(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(10, 10), # 2nd ten can be any number mapping the input size to some space 10x10 has 100 edges/numbers/weights
# linear is all just multiplictions of weights and inputs
nn.Sigmoid(), # sigmoid (or relu) creates a non-linearity threshold
nn.Linear(10, 10), # number of inputs, must be number of outputs, here we map all of the previous outputs to a single output
nn.Sigmoid(),
nn.Linear(10, 1), # number of inputs, must be number of outputs, here we map all of the previous outputs to a single output
nn.Sigmoid(),
)
self.optimizer = Adam(self.parameters()) # after you have the gradient, now we have to choose how to update the weights
self.loss = nn.MSELoss() # how we compare the predicted and actual values
def forward(self, X):
return self.layers(X)
def fit(self, X, y_true):
self.optimizer.zero_grad() # zero the gradients
y_pred = self.forward(X) # feed the network with an input
loss = self.loss(y_true, y_pred) # then calculate the loss (error between prediction and groudn truth)
loss.backward() # backwards calculates the gradients over every weight
self.optimizer.step() # then updates the weights
return loss.item()
# Initialize the data and model
xs, ys = create_data()
# create a copy of the data
real_solution = ys.copy()
xs = Tensor(xs)
ys = Tensor(ys)
print(xs.shape)
print(ys.shape)
lfsr_model = LFSRModel()
# print(lfsr_model(xs))
# Now train the model, printing every mod epochs to get an idea of convergence
EPOCHS = 5000
for epoch in range(EPOCHS):
loss = lfsr_model.fit(xs, ys)
if epoch % 1000 == 0:
print(loss)
# Now predict again
new_model = lfsr_model(xs)
new_model = np.round(new_model.detach().numpy()) # round the values to 0 or 1
# return all indeces where real_sum and predicted_sum are different
# this is where the model is wrong
wrong = np.where(new_model != real_solution)[0]
# fprint
print("Number of wrong predictions: " + str(len(wrong)))
print("Indeces where solution and predicted differ: " + str(wrong))
print("Inputs with incorrect outputs: " + str(xs[wrong]))
print("Predicted Values: " + str(new_model[wrong]))
print("Solution Values: " + str(real_solution[wrong]))
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import torch as T
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
'''
17_4:
Use policy gradient reinforcement learning to balance an inverted pendulum mounted on a cart driven by a controller that you train.
References:
https://lilianweng.github.io/posts/2018-04-08-policy-gradient/
https://www.youtube.com/watch?v=wc-FxNENg9U&t=87s
We have two actions, push left or push right.
We have four states, cart position, cart velocity, pole angle, and pole velocity at tip.
Pole angle and velocity are also symmetric
'''
class DeepQNetwork(nn.Module):
def __init__(self, lr,input_dims, fc1_dims, fc2_dims, n_actions):
super(DeepQNetwork, self).__init__()
self.input_dims = input_dims
self.fc1_dims = fc1_dims
self.fc2_dims = fc2_dims
self.n_actions = n_actions
self.fc1 = nn.Linear(*self.input_dims,self.fc1_dims)
self.fc2 = nn.Linear(self.fc1_dims,self.fc2_dims)
self.fc3 = nn.Linear(self.fc2_dims,self.n_actions)
self.optimizer = optim.Adam(self.parameters(),lr=lr)
self.loss = nn.MSELoss()
self.device = T.device('cuda:0' if T.cuda.is_available() else 'cpu')
self.to(self.device)
def forward(self,state):
x = F.relu(self.fc1(state))
x = F.relu(self.fc2(x))
actions = self.fc3(x)
return actions
class Agent():
def __init__(self, gamma, epsilon, lr, input_dims, batch_size, n_actions,
max_mem_size=100000, eps_end=0.01, eps_dec=5e-4):
self.gamma = gamma
self.epsilon = epsilon
self.eps_min = eps_end
self.eps_dec = eps_dec
self.lr = lr
self.action_space = [i for i in range(n_actions)]
self.mem_size = max_mem_size
self.batch_size = batch_size
self.mem_cntr = 0
self.Q_eval = DeepQNetwork(self.lr, n_actions=n_actions, input_dims=input_dims,
fc1_dims=256, fc2_dims=256)
self.state_memory = np.zeros((self.mem_size,*input_dims), dtype=np.float32)
self.new_state_memory = np.zeros((self.mem_size,*input_dims), dtype=np.float32)
self.action_memory = np.zeros(self.mem_size, dtype=np.int32)
self.reward_memory = np.zeros(self.mem_size, dtype=np.float32)
self.terminal_memory = np.zeros(self.mem_size, dtype=np.bool) # whether the state is terminal or not (game is done or not)
def store_transition(self, state, action, reward, state_, terminal):
index = self.mem_cntr % self.mem_size
self.state_memory[index] = state
self.new_state_memory[index] = state_
self.reward_memory[index] = reward
self.terminal_memory[index] = terminal
self.action_memory[index] = action
self.mem_cntr += 1
def choose_action(self, observation):
if np.random.random() > self.epsilon:
state = T.tensor([observation]).to(self.Q_eval.device)
actions = self.Q_eval.forward(state)
action = T.argmax(actions).item()
else:
action = np.random.choice(self.action_space)
return action
def learn(self):
"""
Get's called every step
"""
if self.mem_cntr < self.batch_size:
return
self.Q_eval.optimizer.zero_grad()
max_mem = self.mem_cntr if self.mem_cntr < self.mem_size else self.mem_size
batch = np.random.choice(max_mem,self.batch_size,replace=False)
batch_index = np.arange(self.batch_size,dtype=np.int32)
state_batch = T.tensor(self.state_memory[batch]).to(self.Q_eval.device)
new_state_batch = T.tensor(self.new_state_memory[batch]).to(self.Q_eval.device)
reward_batch = T.tensor(self.reward_memory[batch]).to(self.Q_eval.device)
terminal_batch = T.tensor(self.terminal_memory[batch]).to(self.Q_eval.device)
action_batch = self.action_memory[batch]
q_eval = self.Q_eval.forward(state_batch)[batch_index,action_batch]
q_next = self.Q_eval.forward(new_state_batch)
q_next[terminal_batch] = 0.0
q_target = reward_batch + self.gamma*T.max(q_next,dim=1)[0] # returns a tuple of (values,indices), we want the value so [0]
loss = self.Q_eval.loss(q_target,q_eval).to(self.Q_eval.device)
loss.backward()
self.Q_eval.optimizer.step()
self.epsilon = self.epsilon - self.eps_dec if self.epsilon > self.eps_min else self.eps_min
import gym
if __name__ == '__main__':
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# Week 12 - Search
## Rosenbrock search with Nelder Mead
<img src="img/nelder_mead.jpg" height="500">
## Rosenbrock search with Gradient Descent
Had to add pretty substantial inertia to get all the way to the edge of the banana
<img src="img/grad_descent.jpg" height="500">
## Rosenbrock search with CMA_ES
Using the nicely packaged cmaes library here
<img src="img/cmaes_rosenbrock.gif" height="500">
To be sure I searched a parabola as well:
<img src="img/cmaes_parabola.gif" height="500">
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# Week 6 - Finite Elements # Week 6 - Finite Elements
## 10.2 <img src="img/6_2.png" height="500">
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