Pop-up effect or Scaling effect with SVG

Pop-up effect or Scaling effect with SVG

By : joeschmoe2
Date : November 22 2020, 11:17 AM
wish of those help I've created several shapes in SVG and added an effect that changes the shapes color when hovered over. , I've got it to do what I wanted by adding
code :
target.setAttributeNS(null, 'transform', "translate(0 0)");
function unselectedColour(evt) {
var target = evt.target;
target.setAttributeNS(null, 'fill', 'white');
target.setAttributeNS(null, 'transform', "translate(0 0)");


function selectedColourBuilding(evt) {
var target = evt.target;
target.setAttributeNS(null, 'fill', 'purple');
target.setAttributeNS(null, 'transform', "translate(-3 -3)");


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Scaling buttons without losing the 3D effect

Scaling buttons without losing the 3D effect

By : user3882267
Date : March 29 2020, 07:55 AM
wish of those help You could make 9-patch button backgrounds using draw-9 patch in the Android SDK bin folder. These will scale very nicely from a very small sample/drawable.
Image scaling and fading effect

Image scaling and fading effect

By : Robert the Bruce
Date : March 29 2020, 07:55 AM
it fixes the issue How can i on click change a image with the same effect as of firefox download complete green arrow. ie scaling and fading effect at the same instrance , You can clone the element then use .animate() like
code :
  var new_img = $(this).clone();
    "width" : "120px",
    "height" : "120px",
    "top" : $(this).offset().top,
    "left" : $(this).offset().left,
    "position" : "absolute"
    "width" : "40px",
    "height" : "40px",
    "opacity" : "0"
var that = $(this);
that.attr("src" , "IMG_URL");
Effect of feature scaling on accuracy

Effect of feature scaling on accuracy

By : Andreas Boeck
Date : March 29 2020, 07:55 AM
Hope that helps
I thought that feature scaling would increase the accuracy, especially given the large differences in features.
How to create scaling effect for button in CSS?

How to create scaling effect for button in CSS?

By : RamGunta
Date : March 29 2020, 07:55 AM
seems to work fine You can use :before pseudo-element to create another rectangle under button and use transform to scale it on click. Instead of css :active state you can use click event. After you click on element you need to click on some other element to remove active state and see effect again on click.
code :
button {
  padding: 10px 25px;
  background: red;
  color: white;
  margin: 50px;
  border: none;
  position: relative;
  outline: none;
button:before {
  content: '';
  position: absolute;
  left: 0;
  top: 0;
  height: 100%;
  width: 100%;
  background: black;
  z-index: -1;
button:focus:before {
  transform: scale(2);
  transition: all 0.4s linear;
  opacity: 0;
Effect of scaling Features when NaNs are set to -1

Effect of scaling Features when NaNs are set to -1

By : user1300795
Date : March 29 2020, 07:55 AM
hop of those help? I know that you got the answer from the comments above, but in an effort to show new scikit-learn users how you might approach a problem like this, I've put together a very rudimentary solution that demonstrates how to build a custom transformer that would handle this:
code :
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils.validation import check_array, check_is_fitted
import numpy as np

class NanImputeScaler(BaseEstimator, TransformerMixin):
    """Scale an array with missing values, then impute them
    with a dummy value. This prevents the imputed value from impacting
    the mean/standard deviation computation during scaling.

    with_mean : bool, optional (default=True)
        Whether to center the variables.

    with_std : bool, optional (default=True)
        Whether to divide by the standard deviation.

    nan_level : int or float, optional (default=-99.)
        The value to impute over NaN values after scaling the other features.
    def __init__(self, with_mean=True, with_std=True, nan_level=-99.):
        self.with_mean = with_mean
        self.with_std = with_std
        self.nan_level = nan_level

    def fit(self, X, y=None):
        # Check the input array, but don't force everything to be finite.
        # This also ensures the array is 2D
        X = check_array(X, force_all_finite=False, ensure_2d=True)

        # compute the statistics on the data irrespective of NaN values
        self.means_ = np.nanmean(X, axis=0)
        self.std_ = np.nanstd(X, axis=0)
        return self

    def transform(self, X):
        # Check that we have already fit this transformer
        check_is_fitted(self, "means_")

        # get a copy of X so we can change it in place
        X = check_array(X, force_all_finite=False, ensure_2d=True)

        # center if needed
        if self.with_mean:
            X -= self.means_
        # scale if needed
        if self.with_std:
            X /= self.std_

        # now fill in the missing values
        X[np.isnan(X)] = self.nan_level
        return X
nan = np.nan
data = np.array([
    [ 1., nan,  3.],
    [ 2.,  3., nan],
    [nan,  4.,  5.],
    [ 4.,  5.,  6.]
>>> imputer = NanImputeScaler().fit(data)
>>> imputer.means_
array([ 2.33333333,  4.        ,  4.66666667])
>>> imputer.std_
array([ 1.24721913,  0.81649658,  1.24721913])
>>> imputer.transform(data)
array([[ -1.06904497, -99.        ,  -1.33630621],
       [ -0.26726124,  -1.22474487, -99.        ],
       [-99.        ,   0.        ,   0.26726124],
       [  1.33630621,   1.22474487,   1.06904497]])
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
pipe = Pipeline([
        ("scale", NanImputeScaler()),
        ("clf", LogisticRegression())
    ]).fit(data, y)
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