descent | Descent by Parallax Software Corp | JSON Processing library
kandi X-RAY | descent Summary
kandi X-RAY | descent Summary
Descent (1995) by Parallax Software Corp.
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descent Examples and Code Snippets
Lines of Code : 159
License : No License
Copydef main():
train, test = get_data()
# Need to scale! don't leave as 0..255
# Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1)
# So flatten it and make it 0..9
# Also need indicator matrix for cost calculation
Xtra def main():
train, test = get_data()
# Need to scale! don't leave as 0..255
# Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1)
# So flatten it and make it 0..9
# Also need indicator matrix for cost calculation
def fit(self, X, learning_rate=1e-2, max_iter=10):
# train the HMM model using gradient descent
N = len(X)
D = X[0].shape[1] # assume each x is organized (T, D)
pi0 = np.ones(self.M) / self.M # initial state distribu Community Discussions
Trending Discussions on descent
QUESTION
BRAND new to ML. Class project has us entering the code below. First I am getting warning:
...ANSWER
Answered 2021-Jun-12 at 04:26You need to set self.theta to be an array, not a scalar (at least in this specific problem).
In your case, (intercepted-augmented) X is a '3 by n' array, so try self.theta = [0, 0, 0] for example. This will correct the specific error 'bool' object has no attribute 'mean'. Still, this will just produce preds as a zero vector; you haven't fit the model yet.
To let you know how I approached the error, I first went to the exact line the error message was pointing to, and put print(preds == y) before the line, and it printed out False. I guess what you expected was a vector of True and Falses. Your y seemed okay; it was a vector (a list to be specific). So I tried print(pred), which showed me a '3 by n' array, which is weird. Now going up from that line, I found out that pred comes from predict_prob(), especially np.dot(X, self.theta). Here, when X is a '3 by n' array and self.theta is a scalar, numpy seems to multiply the scalar to each item in the array and return the array (having the same dimension as the original array), instead of doing matrix multiplication! So you need to explicitly provide self.theta as an array (conforming to the dimension of X).
Hope the answer and the reasoning behind it helped.
As for the red line you mentioned in the comment, I guess it is also because you are not fitting the model. (To see the problem, put print(probs) before plt.countour(...). You'll see an array with 0.5 only.)
So try putting model.fit(X, y) before preds = model.predict(X). (You'll also need to put self.verbose = verbose in the __init__().)
After that, I get the following:
QUESTION
I have a data having 3 features and 1 target variable. I am trying to use gradient descent and later minimize the RMSE
While trying to run the code, I am getting nan as the cost/error term Tried a lot of methods but can't figure it out.
Can anyone please tell me where I am going wrong with the calculation.
Here's the code:
m = len(y)
ANSWER
Answered 2021-Jun-07 at 19:01The only reasonable solution which we came up was since the cost was high.. it was not possible to use this approach for this solution. We tried using a different approach like simple linear regression and it worked.
QUESTION
Devs! I am using PdfDocument to try to save the text as a pdf file. So I wrote this code :
...ANSWER
Answered 2021-Jun-07 at 03:01When you start a new page you are not assigning it to a variable
Change to
QUESTION
I have a struct:
...ANSWER
Answered 2021-Jun-05 at 20:33Build a map from field name to std::function(std::string const&)>, like:
QUESTION
I am currently working on building a CNN for sound classification. The problem is relatively simple: I need my model to detect whether there is human speech on an audio record. I made a train / test set containing records of 3 seconds on which there is human speech (speech) or not (no_speech). From these 3 seconds fragments I get a mel-spectrogram of dimension 128 x 128 that is used to feed the model.
Since it is a simple binary problem I thought the a CNN would easily detect human speech but I may have been too cocky. However, it seems that after 1 or 2 epoch the model doesn’t learn anymore, i.e. the loss doesn’t decrease as if the weights do not update and the number of correct prediction stays roughly the same. I tried to play with the hyperparameters but the problem is still the same. I tried a learning rate of 0.1, 0.01 … until 1e-7. I also tried to use a more complex model but the same occur.
Then I thought it could be due to the script itself but I cannot find anything wrong: the loss is computed, the gradients are then computed with backward() and the weights should be updated. I would be glad you could have a quick look at the script and let me know what could go wrong! If you have other ideas of why this problem may occur I would also be glad to receive some advice on how to best train my CNN.
I based the script on the LunaTrainingApp from “Deep learning in PyTorch” by Stevens as I found the script to be elegant. Of course I modified it to match my problem, I added a way to compute the precision and recall and some other custom metrics such as the % of correct predictions.
Here is the script:
...ANSWER
Answered 2021-Jun-02 at 12:50Read it once more and let it sink.
Do you understand now what is the problem?
A convolution layer learns a static/fixed local patterns and tries to match it everywhere in the input. This is very cool and handy for images where you want to be equivariant to translation and where all pixels have the same "meaning".
However, in spectrograms, different locations have different meanings - pixels at the top part of the spectrograms mean high frequencies while the lower indicates low frequencies. Therefore, if you have matched some local pattern to a local region in the spectrogram, it may mean a completely different thing if it is matched to the upper or lower part of the spectrogram. You need a different kind of model to process spectrograms. Maybe convert the spectrogram to a 1D signal with 128 channels (frequencies) and apply 1D convolutions to it?
QUESTION
We desire to create a pushdown automaton (PDA) that uses the following "alphabet" (by alphabet I mean a unique set of symbol strings/keys):
...ANSWER
Answered 2021-May-28 at 13:55In pseudocode, a DPDA can be implemented like this:
QUESTION
I'm currently working on gradient descent projects.
I chose nba stats as my data so I downloaded 3Pts data and pts data from basketball reference and I have successfully plotted a scatter plot. However, the result does not seem right.
My scatter plot is going towards right-upside (since more 3points made generally means more points scored, so it makes sense)
But my gradient descent line is going to left-upside, I don't know what's wrong.
...ANSWER
Answered 2021-May-25 at 12:18A few things in this code don't really make sense. Are you trying to do the regression from scratch? Because you do import scikit learn but never apply it. You can refer to this link on how to use scikit learn regression here. I would also consider playing around with other algorithms too.
I believe this is what you are trying to do here:
QUESTION
according to this graph: desmos
...ANSWER
Answered 2021-May-22 at 15:42sym.solve solves an equation for the independent variable. If you provide an expression, it'll assume the equation sym.Eq(expr, 0). But this only gives you the x values. You have to substitute said solutions to find the y value.
Your equation has 3 solutions. A conjugate pair of complex solutions and a real one. The latter is where your two graphs meet.
QUESTION
I have a question regarding SGD Optimizer.
There are 3 types of Gradient Descent Algorithm:
- Batch Gradient Descent
- Mini-Batch Gradient Descent and
- Stochastic Gradient Descent
Stochastic Gradient Descent is an Algorithm in which one Instance from Training Set is taken at Random and the Weights are updated with respect to that Instance.
SGD Optimizer is slightly deviating from the above definition where it can accept the batch_size of more than 1. Can someone clarify this deviation?
Below code seems to be in line with the definition of Stochastic Gradient Descent:
ANSWER
Answered 2021-May-21 at 16:59Quote from Wikipedia:
It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated from a randomly selected subset of the data)
So the three types you mentioned are all SGD. Even if you use all your data to perform an SGD iteration, it's still a stochastic estimate of the actual gradient; as upon collecting new data (your dataset doesn't include all the data in universe) your estimate will change, hence stochastic.
QUESTION
Okay this is a bit of an involved question, but tl;dr it's basically how do you parse an "actual tree" using a "pattern tree"? How do you check if a particular tree instance is matched by a specific pattern tree?
To start, we have the structure of our pattern tree. The pattern tree can generally contain these types of nodes:
sequencenode: Matches a sequence of items (zero or more).optionalnode: Matches one or zero items.classnode: Delegates to another pattern tree to match.firstnode: Matches the first child pattern it finds out of a set.interlacenode: Matches any of the child patterns in any order.textnode: Matches direct text.
That should be good enough for this question. There are a few more node types, but these are the main ones. Essentially it is like a regular expression or grammar tree.
We can start with a simple pattern tree:
...ANSWER
Answered 2021-May-17 at 10:49The easiest way - just to convert your 'pattern tree' to regexp, and then check text representation of your 'actual tree' with that regexp.
Regarding the Recursive descent. Recursive descent itself is enough to perform the grammar check, but not very effective, because sometimes you need to check pattern from beginning multiple times. To make single-pass grammar checker you need state machine as well, and that is what Regexp has under the hood.
So no need to reinvent the wheel, just specify your 'pattern' as regexp (either convert your representation of the pattern to regexp)
Your
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