MultiModal | Use multiple .sheet , .alert , etc | Frontend Framework library
kandi X-RAY | MultiModal Summary
kandi X-RAY | MultiModal Summary
By default, SwiftUI views with multiple modal modifiers (e.g. .sheet, .alert) in the same body will only use the last one in the chain of modifiers and ignore all previous ones. MultiModal brings a .multiModal modifier to declare multiple modal modifiers in the same view body.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of MultiModal
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MultiModal Examples and Code Snippets
Lines of Code : 34License : Permissive (Unlicense)
Copystruct NoMultiModalDemoView: View {
@State var sheetAPresented = false
@State var sheetBPresented = false
@State var sheetCPresented = false
var body: some View {
VStack(spacing: 20) {
Button("Sheet A") { sheetAPr Community Discussions
Trending Discussions on MultiModal
QUESTION
I'm obviously dealing with slightly more complex and realistic data, but to showcase my trouble, let's assume we have these data:
...ANSWER
Answered 2022-Mar-31 at 12:52IIUC, try:
QUESTION
I am bootstrapping a new app with Jetpack Compose and Material 3. I've created a bunch of new apps lately with this configuration, so this problem has me stuck: I cannot get the IDE's compose previews to show a background or system UI. The compiled app works great.
I tried building this sample app with Jetpack Compose + Material 3 I created a while back and all of the previews are just fine in the same version of the IDE. I also tried downgrading my libraries to match that sample app's libraries. No luck. The sample app has working previews, mine does not.
I DO have the compose UI tooling included in my Gradle script for my debug variant, and I am previewing with the debug variant.
Thoughts?
Here is what I see:
This is how I generate this screen code sample:
...ANSWER
Answered 2022-Feb-17 at 21:25I think that your code should looks like this:
QUESTION
I have a set of variables X1 and X2 and Y with relationship plot as shown below. X2 values are used for color coding.
X1, X2, and X3 are integer variables.
The observed pattern is multimodal.
What is the best way to predict Y based on X1 and X2?
Can we use non-linear or hurdle models for this?
Also what are the tools available to achieve this in R?
...ANSWER
Answered 2022-Feb-17 at 16:29Generally speaking, there is no need to worry about the distribution of the response. Although you are showing a bivariate plot, it is possible that the multi-modality is explained by X2 (or other, missing variables)
It is the distribution of the model residuals that matters (if it matters at all).
If the residuals are non-normal, then certain inferences may be invalid, although this may not be a problem at all if the model is used for prediction.
If you really do have a curvilinear association then you could consider:
- transformations
- non-linear terms
- splines
- generalised additive models (GAMs)
- non-linear models
Of course, if the underlying problem is that you have missing explanatory variables, then some of these approaches may lead to an overfitted model.
QUESTION
I'm trying to make a code that returns the mode of a list of given numbers. Here's the code :
...ANSWER
Answered 2021-Oct-31 at 11:40When you return from the current function, any owned values are destroyed (other than the ones being returned from the function), and any data referencing that destroyed data therefore cannot be returned, e.g.:
QUESTION
I have data of 50 samples per time series. I want to build a time series classifier.
Each sample has three inputs - a vector with the shape 1X768, a vector with the shape 1X25, a vector with the shape 1X496.
Each input is from different modality so need to go through some input-specific layers before concatenate all of them.
The data is store in the dataframe:
...ANSWER
Answered 2021-Aug-31 at 08:47Use some networks (Linear, MLPs, etc.) to embed them to same dimension and you can use add, elementwisely multiply, bi(tri)linear or whatever you want to get these together into dimension-unified input for RNNs or CNNs. Or you can just concat each timestep, and it's one data per timestep, and it'll be fine for CNNs
QUESTION
I am using MOSI dataset for the multimodal sentiment analysis, where for now I am training the model for text dataset only. For text, I am using glove embeddings of 300 dimensions for processing text. My total vocab size is 2173 and my padded sequence length is 30. My target array is [0,0,0,0,0,0,1] where left most is highly -ve and right most highly +ve.
I am splitting the dataset like this
X_train, X_test, y_train, y_test = train_test_split(WDatasetX, y7, test_size=0.20, random_state=42)
My tokenization process is
...ANSWER
Answered 2021-Aug-11 at 19:22A large difference between Train and Validation stats typically indicates overfitting of models to the Train data.
To minimize this I do a few things
- reduce the size of the model.
- Add a few dropout or similar layers in the model. I have had good success with using these layers:
layers.LeakyReLU(alpha=0.8),
See guidance here: https://www.tensorflow.org/tutorials/keras/overfit_and_underfit#strategies_to_prevent_overfitting
QUESTION
I am trying to make a next-word prediction model with LSTM + Mixture Density Network Based on this implementation(https://www.katnoria.com/mdn/).
Input: 300-dimensional word vectors*window size(5) and 21-dimensional array(c) representing topic distribution of the document, used to train hidden initial states.
Output: mixing coefficient*num_gaussians, variance*num_gaussians, mean*num_gaussians*300(vector size)
x.shape, y.shape, c.shape with an experimental 161 obserbations gives me such:
(TensorShape([161, 5, 300]), TensorShape([161, 300]), TensorShape([161, 21]))
...ANSWER
Answered 2021-Jun-14 at 19:07for MDN model , the likelihood for each sample has to be calculated with all the Gaussians pdf , to do that I think you have to reshape your matrices ( y_true and mu) and take advantage of the broadcasting operation by adding 1 as the last dimension . e.g:
QUESTION
I would like to create a graph. To do this, I have created a JSON file. The Skills (java, python, HTML, json) should be the links and the index (KayO, BenBeck) should be the nodes. Also the node must not fall below a certain minimum size and must not become too large.
After that, I would like to be able to call up the list of publications on the right-hand side by clicking on the node. The currently selected node in the visualisation should be highlighted.
I have already implemented from this example (https://bl.ocks.org/heybignick/3faf257bbbbc7743bb72310d03b86ee8). But unfortunately I can't get any further.
The error message I always get is:
Uncaught TypeError: Cannot read property 'json' of undefined
This is what my issue currently looks like:
The JSON file:
...ANSWER
Answered 2021-May-15 at 14:59Your JSON file should be of format:
QUESTION
I've used GaussianMixture to analyze a multimodal distribution. From the GaussianMixture class I can access the means and covariances using the attributes means_ and covariances_. How can I use them to now plot the two underlying unimodal distributions?
I thought of using scipy.stats.norm but I don't know what to select as parameters for loc and scale. The desired output would be analogously as shown in the attached figure.
The example code of this question was modified from the answer here.
...ANSWER
Answered 2021-Mar-14 at 16:19It is not entirely clear what you are trying to accomplish. You are fitting a GaussianMixture model to the concatenation of the sum of the values of pdfs of two gaussians sampled on a uniform grid, and the unifrom grid itself. This is not how a Gaussian Mixture model is meant to be fitted. Typically one fits a model to random observations drawn from some distribution (typically unknown but could be a simulated one).
Let me assume that you want to fit the GaussianMixture model to a sample drawn from a Gaussian Mixture distribution. Presumably to test how well the fit works given you know what the expected outcome is. Here is the code for doing this, both to simulate the right distribution and to fit the model. It prints the parameters that the fit recovered from the sample -- we observe that they are indeed close to the ones we used to simulate the sample. Plot of the density of the GaussianMixture distribution that fits to the data is generated at the end
QUESTION
I'm trying to get the patched regions of a citrus fruit using Otsu method with opencv. According to this paper: https://www.intechopen.com/books/agricultural-robots-fundamentals-and-applications/multimodal-classification-of-mangoes/ these authors were using the Green channel (G) to get the patches regions of mangoes:
I'm doing the same but usign lemons but I can't get those regions of my lemon.
This is my input image:
First I read the image and I'm calling to a function to show the image:
ANSWER
Answered 2021-Feb-17 at 14:30Edit: I inverted the second mask to get just the defect areas.
Once you use otsu's the first time it'll give you back a mask that separates the foreground (the fruit) and the background. You can use otsu's a second time on the masked area to get another mask that separates out the dark spots on the fruit.
Unfortunately, OpenCV doesn't have a simple way of running otsu's on just a masked area. However, otsu's is just looking for thresholds on the pixel intensity histogram that create the greatest interparty variance. Since this histogram is all proportional, we can force otsu's to run on just the masked area by making all of the unmasked pixels match the histogram propotions.
I converted to HSV and used the saturation channel to separate the fruit from the background.
I then used the histogram to replicate the pixel proportions on the unmasked pixels of the hue channel.
Hue Before
Hue After
Then I run otsu's a second time on the hue channel.
Now to get the final mask, we just bitwise_and the first and second masks together (and do an opening and closing operation to clean up little holes)
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