milk | MILK : Machine Learning Toolkit | Machine Learning library
kandi X-RAY | milk Summary
kandi X-RAY | milk Summary
MILK: Machine Learning Toolkit
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Top functions reviewed by kandi - BETA
- Compute nfold cross validation
- Generator for fold generator
- Normalise labels
- Train the model with the given features and labels
- Function for sigmoid
- NnmF function
- Return a random number generator
- Select kmeans from ks
- Performs k - means clustering
- Compute the Mahalanobis2 operator
- Calculates the correlation coefficient for 1 - class 1
- Compute the Bayesian Information Criteria
- Compute the BIC model
- R Compute expected Impact
- Compute principal components
- Train the supervised model
- Run the main loop
- Get number of available CPUs
- Train a forest model
- Run the procedure
- Train codebook
- Negates the neg_z1_loss between two labels
- Applies a function to each model
- Lasso walk function
- Compute the likelihood
- Trains the classification model
milk Key Features
milk Examples and Code Snippets
Community Discussions
Trending Discussions on milk
QUESTION
I am trying to solve an optimization problem using the GEKKO solver on Python, but keep obtaining an NAN objective value, even though the problem is successfully solved. There seems to be something I am missing, but I have not been able to identify what it is. I tried to make sure that nothing is being divided by zero, but as far as I can tell, that is not the case. The code I have is:
...ANSWER
Answered 2022-Apr-04 at 02:19The optimizer tries to maximize the objective function by making (foodvars[i][0]+foodvars[i][1]-foodvars[i][2])
approach zero. This gives an infinity objective function that results in the NaN
result. Two suggestions:
- Multiply both sides of equation 2 by
(10**6*(1-fooditems_params[i][1])*(1-fooditems_params[i][2]))
to remove the potential divide-by-zero. - Set bounds on
foodvars
to prevent divide-by-zero in the objective.
QUESTION
I have basic enum
...ANSWER
Answered 2022-Jan-09 at 21:25You can use the static method cases()
for this. This returns an array of all values in the enum. The values have a "name" property that is a string representation you can check against (backed enums also have a "value" property that contains the string value you defined in the enum).
So an example implementation could be something like:
QUESTION
I have the following dataset:
...ANSWER
Answered 2022-Mar-17 at 05:21A possible crossprod
uct solution on a tabulation of the transaction
and product
. I'm not sure how well it will scale, but it seems to work:
QUESTION
Let's assume we have the following data frame df
:
ANSWER
Answered 2022-Mar-11 at 22:17You can pass a lambda function .loc
which will filter the dataframe for only rows that match the condition that the lambda function returns:
QUESTION
I have an example shopping list:
...ANSWER
Answered 2022-Feb-16 at 18:58You want "match any set of digits that is not followed by optional whitespace and x`. For that, you need a "negative lookahead":
QUESTION
I have the following dataset
...ANSWER
Answered 2022-Feb-15 at 00:25Here is one solution:
QUESTION
I have a vector of strings of the following kind:
...ANSWER
Answered 2022-Jan-11 at 08:26With Office 365, you can do:
QUESTION
I'm trying to edit the facet label's text produced by emmean's arrow plot using ggplot2's syntax to override the default.
...ANSWER
Answered 2021-Dec-18 at 20:44You were almost there. (1) You need a named vector (I think), not a list; (2) the names of the list should match the elements of the faceting variable, not the already-labeled values (i.e. the strip labels).
QUESTION
I have a json file with which has an array of product items i want to split them based on the category of the item, This is my json file looks like,
...ANSWER
Answered 2021-Dec-17 at 16:59In general, you violated the Single Responsibility principal (SOLID), because your code do two different things together: parse a file and categorize items. You should split these responsibilities.
One of ways how you do this is to create classes that represents your data. Let's assume that these classes are Item
, Category
, Order
and CategorizedItems
. Order
is a class that represents your source JSONObject
and CategorizedItems
- represents your result JSONArray
.
In this case, you should firstly parse the file into these classes and only after that transform them into JSONArray
.
Code sample:
Data classes:
QUESTION
sweets = {'cadbury': [180,90], 'candy': [190],
'milk chocolate': [150, 160], 'dark chocolate': [100],
'white chocolate': [180], 'ice cream': [122]}
...ANSWER
Answered 2021-Dec-10 at 14:11Using numpy you could try:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install milk
You can use milk like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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