momentum | Momentum Trading Framework
kandi X-RAY | momentum Summary
kandi X-RAY | momentum Summary
Momentum Investment Framework in R for my masters thesis. Updates will be pushed after the thesis defense!. Enhanced momentum strategy ranking framework using the following second order characteristics.
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QUESTION
I have my model and inputs moved on the same device but I still get the runtime error :
...ANSWER
Answered 2022-Feb-27 at 07:14TL;DR use nn.ModuleList instead of a pythonic one to store the hidden layers in Net.
All your hidden layers are stored in a simple pythonic list self.hidden in Net. When you move your model to GPU, using .to(device), pytorch has no way to tell that all the elements of this pythonic list should also be moved to the same device.
however, if you make self.hidden = nn.ModuleLis(), pytorch now knows to treat all elements of this special list as nn.Modules and recursively move them to the same device as Net.
QUESTION
I am currently working on an neuronal network that can classify cats and dog and everything thats not cat nor dog. And my programm has this: error i can't solve:
" File "/home/johann/Schreibtisch/NN_v0.01/classification.py", line 146, in train(epoch) File "/home/johann/Schreibtisch/NN_v0.01/classification.py", line 109, in train loss = criterion(out, target) File "/home/johann/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/johann/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py", line 1047, in forward return F.cross_entropy(input, target, weight=self.weight, File "/home/johann/.local/lib/python3.8/site-packages/torch/nn/functional.py", line 2693, in cross_entropy return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction) File "/home/johann/.local/lib/python3.8/site-packages/torch/nn/functional.py", line 2388, in nll_loss ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index) RuntimeError: 1D target tensor expected, multi-target not supported"
The code:
...ANSWER
Answered 2022-Feb-16 at 15:35The reason behind this error is that your targets list are list of lists like that:
QUESTION
heres my code
...ANSWER
Answered 2022-Jan-02 at 06:33You're trying to access screen[1], but screen is of type pygame.Surface. To get the height of screen, use screen.get_height() instead:
if player_location[1] > screen.get_height() - player_image.get_height():
QUESTION
In short: what I only need is this graphic map and the team symbol, without the other data appearing on the screen, wasting space and without the scrollbar on the right side that covers the end of the graphic.
...ANSWER
Answered 2021-Nov-19 at 13:58You can use the scroll(x, y) function:
scroll(0, 10000)
QUESTION
I've started to write a physics engine but became stuck on some physics of resolving collisions. Let's say I have this situation:
I.e. body B is going towards body A at the speed of 1 space units/time unit. Both A and B have the same mass of 1 unit. Let's consider a completely elastic collision.
I've read in a book (Game Physics Engine Development) that an impulse-based approach can be used to resolve the collision (i.e. find out the linear and angular velocities of both bodies after the collision). As I understand it, it should work like this:
- When the bodies collide, I get the point of the collision and the collision normal.
- At the point of the collision I consider only two points colliding in the direction of the normal (the points at which the bodies are touching, i.e. I ignore the shapes of both bodies) and I compute the new velocities of these two colliding points (this is easy to do, there is a simple formula found e.g. on Wikipedia).
- I find an impulse such that when applied to both bodies at this point it achieves the computed velocities for these two points.
Now the problem arises when I consider that from a physical point of view both momentum and kinetic energy need to be conserved. With these constraints in mind there is seemingly no solution, because:
When B collides with A, B should come to complete stop and transfer all its momentum and kinetic energy to A, according to elastic collision formula. In order for linear momentum to stay conserved, A then has to start linearly moving left at the same speed as B was before the collision (as they have the same mass). So now A has the same kinetic energy as B had, which however means that A cannot come into rotation because that would add additional kinetic energy to it (as rotating adds kinetic energy as well as linear motion), breaking the conservation of kinetic energy. Nevertheless, the physically correct solution IS for A to both move linearly to the left AND rotate as B colliding at this location exerts torque (and I've also checked real life object behave this way). Note that we cannot take away some energy of A's linear motion and add it to the rotation as that breaks the conservation of linear momentum.
The only "real" solution is that B doesn't come to complete stop and keeps some momentum while A will be both moving left and rotating. But this doesn't seem to be doable with the impulse-based approach that only takes into account the two colliding points, the elastic collision formula simply say the point at B should come to stop and as B cannot receive any torque (the collision happens in its middle), the only way to fulfill this is for B to stop moving.
So is there something I missed? Is the impulse-based approach just not physically correct? I appreciate any insight and suggestions on how to correctly resolve the collision. Thanks!
...ANSWER
Answered 2021-Nov-29 at 18:23The formulas that you're looking at are for the collision of two point masses. Point masses can't have angular momentum, and so the formulas have no room for that term.
You have to go back to first principles.
Suppose that an edge collides with another body at a point (think corner hitting an edge). Then a specific impulse is imparted at that point, in a direction normal to the edge. (Any other direction would have required friction, which would make this a non-elastic collision.) The opposite impulse is imparted to the other body, along the same vector. Imparting opposite impulses to both bodies is sufficient to guarantee both conservation of momentum and angular momentum. But conservation of energy is going to take some work.
Next, what happens when we impart that momentum? As this physics answer says, we impart momentum as if the impulse happened to the center of mass. We impart angular momentum equal to the cross product of the impulse and the moment arm (the vector describing how much the impulse misses the center of mass). This will cause the body to start rotating at a rate of the impulse divided by the moment of inertia.
You get kinetic energy from the motion of the center of mass, but also kinetic energy from its rotation.
So in your 2-D collision you now have the following facts:
- The mass of each body.
- The velocities of each body.
- The moment of inertia of each velocity.
- The angular velocity of each body.
- The moment arm of the line of force for each body.
You can now calculate the kinetic energy of the whole system, as a function of the magnitude of the specific impulse. Unlike the point mass, ALL of these factors play into it, making the equation complicated. However, like the point mass, you'll get a quadratic equation with 2 solutions. One solution is 0 impulse imparted (representing the system before the collision), and the other is your answer afterwards. Complete with changes to the momentum and angular momentum of both systems.
QUESTION
In the torch.optim documentation, it is stated that model parameters can be grouped and optimized with different optimization hyperparameters. It says that
For example, this is very useful when one wants to specify per-layer learning rates:
...
ANSWER
Answered 2021-Oct-29 at 21:10You can use torch.nn.Sequential to define base and classifier. Your class definition can then be:
QUESTION
I have code that it normally behaves but I want to implement momentum scroll. For the momentum scroll to work, I need to get the position fixed at the body tag. That is not a problem. The problem occurs in different elements with absolute positioning and flex grids.
Do you know a way that I can bypass the fixed positioning?
I am using the butter.js library for implementing momentum scroll but I tested with this codepen and it gives the same result
...ANSWER
Answered 2021-Oct-22 at 09:34Try changing the width of the 'main' element. They are usually coded to fit only elements not the screen
QUESTION
My MongoDB document structure is as follows and some of the factors are NaN.
...ANSWER
Answered 2021-Oct-13 at 05:00I broke out the ol Python to give this a crack - the following code works flawlessly!
QUESTION
I'm reading a paper about Fast-RCNN model.
In the paper section 2.3 part of 'SGD hyper-parameters', it said that All layers use a per-layer learning rate of 1 for weights and 2 for biases and a global learning rate of 0.001
Is 'per-layer learning rate' same as 'layer-specific learning rate' that give different learning rate by layers? If so, I can't understand how they('per-layer learning rate' and 'global learning rate') can be apply at the same time?
I found the example of 'layer-specific learning rate' in pytorch.
...ANSWER
Answered 2021-Oct-01 at 17:46The per-layer terminology in that paper is slightly ambiguous. They aren't referring to the layer-specific learning rates.
All layers use a per-layer learning rate of 1 for weights and 2 for biases and a global learning rate of 0.001.
The concerned statement is w.r.t. Caffe framework in which Fast R-CNN was originally written (github link).
They meant that they're setting the learning rate multiplier of weights and biases to be 1 and 2 respectively.
Check any prototxt file in the repo e.g. CaffeNet/train.prototxt.
QUESTION
momentum_rate = 0.5
learning_rate = 0.1
neurons = 30
def convolutional_neural_network(x, y):
print("Hyper-parameter values:\n")
print('Momentum Rate =',momentum_rate,'\n')
print('learning rate =',learning_rate,'\n')
print('Number of neurons =',neurons,'\n')
model = Sequential()
#model.summary()
model.add(Conv1D(input_shape=(X.shape[1],X.shape[0]),activation='relu',kernel_size = 1,filters = 64))
model.add(Flatten())
model.add(Dense(neurons,activation='relu')) # first hidden layer
model.summary()
model.add(Dense(neurons, activation='relu'))
model.summary()# second hidden layer
model.add(Dense(neurons, activation='relu'))
model.summary()
model.add(Dense(neurons, activation='relu'))
model.summary()
model.add(Dense(10, activation='softmax'))
model.summary()
sgd = optimizers.SGD(lr=learning_rate, decay=1e-6, momentum=momentum_rate, nesterov=True)
model.summary()
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy',tensorflow.keras.metrics.Precision()])
model.summary()
history = model.fit(X, y, validation_split=0.2, epochs=10)
model.summary()
print("\nTraining Data Statistics:\n")
print("CNN Model with Relu Hidden Units and Cross-Entropy Error Function:")
print(convolutional_neural_network(X,y))
ANSWER
Answered 2021-Jun-19 at 18:41Conv1D is expecting an input_shape of the form (steps, input_dim) (see docs).
Now, if I understand correctly your input_dim=1 because 1320 is the number of samples and 150 the length of the array. In this case, change the input_shape=(X.shape[1], X.shape[2]).
Edit: It's unclear what are you trying to do. The code below is working and shows the expected shapes for your network. But beware that I changed the y dimension in order to match the number of rows and the output layer. I'm not sure of what the y shape (150,) is representing.
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