Summer Soft is an album by American trumpeter Blue Mitchell recorded in 1977 and released on the Impulse! label in 1978.[1]
Contents
1Reception
2Track listing
3Personnel
4References
Reception[edit]
Allmusic awarded the album 2 stars with reviewer Scott Yanow stating "Although there are some fine players on this LP they are largely wasted on inferior material and commercial arrangements".[2]
Professional ratings
Review scores
Source
Rating
Allmusic
[2]
Track listing[edit]
"Try Not to Forget" (Cedar Walton) - 7:20
"Summer Soft" (Stevie Wonder) - 5:55
" A Day at the Mint" (Blue Mitchell) - 6:15
"Love Has Made Me a Dreamer" (Mike Dosco, Esmond Edwards) - 4:50
"Evergreen" (Barbra Streisand, Paul Williams) - 4:37
"30 Degrees to the Wind" (Cedar Walton, Susan Brickell) - 5:53
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1 I having trouble getting my ResourceDictionary.MergedDictionaries to load from app.xaml. My WPF app has a static class with a Main defined and startup object set to it. Within Main I created an instance of App and run it. The override OnStartup fires and the mainwindow.cs InitializeComponent gives the error "Message "Cannot find resource named 'MaterialDesignFloatingActionMiniAccentButton'. If I put the resources in the mainwindow.xaml everything is fine, but I wanted them to load at the app level so I they are not in each page. Any help appreciated. public partial class App protected override void OnStartup(StartupEventArgs e) base.OnStartup(e); var app = new MainWindow(); var context = new MainWindowViewModel(); app.DataContext = context; app.Show(); from the Main.. var app = new App(); app.Run(); app.xaml.. <Application x:Class="GS.Server.App" xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation" xmlns:...
up vote 2 down vote favorite There is a clear pattern that show for two separate subsets (set of columns); If one value is missing in a column, values of other columns in the same subset are missing for any row. Here is a visualization of missing data My tries up until now, I used ycimpute library to learn from other values, and applied Iterforest. I noted, score of Logistic regression is so weak (0.6) and thought Iterforest might not learn enough or anyway, except from outer subset which might not be enough? for example the subset with 11 columns might learn from the other columns but not from within it's members, and the same goes for the subset with four columns. This bar plot show better quantity of missings So of course, dealing with missings is better than dropping rows because It would affect my prediction which does contain the same missings quantity relatively. Any better way to deal with these ? [EDIT] The nullity pattern is confirmed: machine-learning cor...
