kandi X-RAY | Variance Summary
kandi X-RAY | Variance Summary
Variance
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Top functions reviewed by kandi - BETA
- Calculates the variance of a population .
- Returns the variance of population
- Measure the throughput of the given function .
- Prints the population .
- Returns the variance of the population .
- Initialize the population .
- Test the variance of population .
- Test for the variance stream .
- Performs variance join join .
Variance Key Features
Variance Examples and Code Snippets
Lines of Code : 59License : Non-SPDX (Apache License 2.0)
Copydef reduce_variance(input_tensor, axis=None, keepdims=False, name=None):
"""Computes the variance of elements across dimensions of a tensor.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of th def _streaming_sparse_false_negative_at_k(labels,
predictions_idx,
k,
class_id=None,
def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=None): # pylint: disable=missing-docstring
if dtype:
working_dtype = np_utils.result_type(a, dtype)
else:
working_dtype = None
if out is not None:
raise ValueError('Setti Community Discussions
Trending Discussions on Variance
QUESTION
I am creating a function that runs through my variables and determines if they are numeric. If the variable is numeric, I want it to print the mean, median, variance, mode and range. And if it is not numeric, I want it to print just the mode. However it doesn't work not sure if I am using the right function (typeof & class)
I receive below error
...ANSWER
Answered 2021-Jun-15 at 13:10Don't use $ inside functions, we can use [[ to extract a particular columns.
You can modify the function as follows -
QUESTION
I am trying to extract random effect correlation parameters from an lmer output.
This is my model:
...ANSWER
Answered 2021-Jun-15 at 12:38You want to use lme4::VarCorr to extract those values. Here is an example.
QUESTION
I would like to edit the model matrix used by predict.lm() in R to predict main effects but not interactions (but using the coefficients and variance from the full model containing interactions).
I have tried:
...ANSWER
Answered 2021-Jun-14 at 20:19We could calculate the interactions by hand; done easily by first creating the terms trms, then evaluating them in an eval(parse()) approach.
QUESTION
I am using the below query to GROUP BY "ReportingDate" but its not grouping the results.
...ANSWER
Answered 2021-Jun-14 at 23:58Consider below
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 am trying to tune hyperparameters for HistGradientBoostingRegressor in sklearn and would like to know what possible values could be for l2_regularization, the rest of the parameter grid that works for me looks like this -
ANSWER
Answered 2021-Jun-12 at 09:55Indeed, Regularizations are constraints that are added to the loss function. The model when minimizing the loss function will have to also minimize the regularization term. Hence, This will reduce the model variance as it cannot overfit.
Acceptable parameters for l2_regularization are often on a logarithmic scale between 0 and 0.1, such as 0.1, 0.001, 0.0001.
QUESTION
I have a problem about calculating the standard deviation and variance of a tensor which fills with random variables. It throws a message which is related with input error.
Here is my code snippet which is shown below.
...ANSWER
Answered 2021-Jun-11 at 09:02For tf.math.reduce_std and tf.math.reduce_variance input tensor must be in real or complex type. So, just convert your data to float before passing to these functions like this:
QUESTION
Based on the guide Implementing PCA in Python, by Sebastian Raschka I am building the PCA algorithm from scratch for my research purpose. The class definition is:
...ANSWER
Answered 2021-Jun-11 at 12:52When calculating an eigenvector you may change its sign and the solution will also be a valid one.
So any PCA axis can be reversed and the solution will be valid.
Nevertheless, you may wish to impose a positive correlation of a PCA axis with one of the original variables in the dataset, inverting the axis if needed.
QUESTION
I am trying to blur of highest variance point from the image. I wrote code below. 1st part finds the variance of the image. I checked the resultant variance of an image and it is correct. (I used Lena's image) In 2nd part, I find the highest variance coordinates and send to this Function which finds gaussian blur. When I execute this code, it throws "C:\Tmp\blur_highest_variance.py", line 66, in sigma=15) numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'subtract' output from dtype('float64') to dtype('uint8') with casting rule 'same_kind'
I tried a few conversions between types but no avail. Can you show me some direction?
ANSWER
Answered 2021-Jun-10 at 18:48The error message tells us the line and the reason for the error:
Traceback (most recent call last):
File "C:\Tmp\blur_highest_variance.py", line 66, in sigma=15)
numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'subtract' output from dtype('float64') to dtype('uint8') with casting rule 'same_kind'
It is more simple to debug the code using intermediate variables:
For example, use an intermediate named gmask:
QUESTION
Like what nn.Conv2d or nn.AvgPool2d do with a tensor and a kernel size, I would like to calculate the variances of a tensor with a kernel size. How can I achieve this? I guess maybe source code of pytorch should be touched?
...ANSWER
Answered 2021-Jun-08 at 11:57If it's only the variance you are after, you can use the fact that
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Variance
You can use Variance like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Variance component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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