text:
Any step-by-step guide on KAN with code?

Are there any step-by-step guides on how to apply KAN on tabular binary classification datasets??

I used this guide https://www.kaggle.com/code/seyidcemkarakas/kan-tabular-data-binary-classification for my dataset and got this error:
`checkpoint directory created: ./model
saving model version 0.0
TypeError                                 Traceback (most recent call last)
 in <cell line: 41>()
39
40 # KAN model training
---> 41 results = model.train({'train_input': train_input, 'train_label': train_label, 'test_input': val_input, 'test_label': val_label},
42                       metrics=(train_acc, test_acc),
43                       opt="LBFGS", steps=100, loss_fn=torch.nn.CrossEntropyLoss())
TypeError: Module.train() got an unexpected keyword argument 'metrics'`
`import torch
from kan import KAN
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
Converting data to Torch tensor
train_input = torch.tensor(X_train.to_numpy(), dtype=torch.float32)
train_label = torch.tensor(y_train.to_numpy(), dtype=torch.long)
val_input = torch.tensor(X_val.to_numpy(), dtype=torch.float32)
val_label = torch.tensor(y_val.to_numpy(), dtype=torch.long)
test_input = torch.tensor(X_test.to_numpy(), dtype=torch.float32)
test_label = torch.tensor(y_test.to_numpy(), dtype=torch.long)
dataset = {
'train_input': train_input,
'train_label': train_label,
'val_input': val_input,
'val_label': val_label,
'test_input': test_input,
'test_label': test_label
}
Create model
model = KAN(width=[11, 2], grid=10, k=2)
Fuctions for getting accuracy scores while training
def train_acc():
preds = torch.argmax(model(dataset['train_input']), dim=1)
return torch.mean((preds == dataset['train_label']).float())
def test_acc():
preds = torch.argmax(model(dataset['test_input']), dim=1)
return torch.mean((preds == dataset['test_label']).float())
KAN model training
results = model.train({'train_input': train_input, 'train_label': train_label, 'test_input': val_input, 'test_label': val_label},
metrics=(train_acc, test_acc),
opt="LBFGS", steps=100, loss_fn=torch.nn.CrossEntropyLoss())
Predictions of train val and test datasets
test_preds = torch.argmax(model.forward(test_input).detach(),dim=1)
test_labels = test_label
train_preds = torch.argmax(model.forward(train_input).detach(),dim=1)
train_labels = train_label
val_preds = torch.argmax(model.forward(val_input).detach(),dim=1)
val_labels = val_label
Evaluate metrics
print("Train ACC:", accuracy_score(train_labels.numpy(), train_preds.numpy()))
print("Val ACC:", accuracy_score(val_labels.numpy(), val_preds.numpy()))
print("Test ACC:", accuracy_score(test_labels.numpy(), test_preds.numpy()))
Plotting KAN network
model.plot(scale=10)
Learning curve based on ACC and LOSS
plt.figure(figsize=(10, 5))
plt.plot(results["train_acc"], label='Training Accuracy')
plt.plot(results["test_acc"], label='Val Accuracy')
plt.plot(results["train_loss"], label='Training Loss')
plt.plot(results["test_loss"], label='Val Loss')
plt.title('Training and Val Accuracy over Iterations')
plt.xlabel('Iteration')
plt.ylabel('Accuracy & Loss')
plt.legend()
plt.grid(True)
plt.show()`

Hi @apavlo89
I saw your comments on Kaggle on my notebook. Firstly thanks for looking.
Seconly, I run my code again but I dont get any errors. Can you share verisons of your libraries ?

Hi, I'm getting the exact same issue on version 0.2.3 - seems like train is not defined in KAN anymore. Instead, there is now the fit method. However, there I'm getting different errors...
File "c:[redacted]\main.py", line 44, in
results = model.fit(
File "C:[redacted]\lib\site-packages\kan\MultKAN.py", line 946, in fit
self.update_grid(dataset['train_input'][train_id])
File "C:[redacted]\lib\site-packages\kan\MultKAN.py", line 358, in update_grid
self.update_grid_from_samples(x)
File "C:[redacted]\lib\site-packages\kan\MultKAN.py", line 354, in update_grid_from_samples
self.get_act(x)
File "C:[redacted]\lib\site-packages\kan\MultKAN.py", line 1739, in get_act
self.forward(x)
File "C:[redacted]\lib\site-packages\kan\MultKAN.py", line 389, in forward
x_numerical, preacts, postacts_numerical, postspline = self.act_funl
File "C:[redacted]\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:[redacted]\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "C:[redacted]\lib\site-packages\kan\KANLayer.py", line 183, in forward
y = coef2curve(x_eval=x, grid=self.grid, coef=self.coef, k=self.k)  # y shape: (batch, in_dim, out_dim)
File "C:[redacted]\lib\site-packages\kan\spline.py", line 118, in coef2curve
y_eval = torch.einsum('ijk,jlk->ijl', b_splines, coef.to(b_splines.device))
File "C:[redacted]\lib\site-packages\torch\functional.py", line 380, in einsum
return _VF.einsum(equation, operands)  # type: ignore[attr-defined]
RuntimeError: expected scalar type Float but found Double

Since 0.2.0, you will need to use the fit method instead of train.

Hi @apavlo89,
Since 0.2.0, you will need to use the fit method instead of train.

Yes, @apavlo89 Could you try it?

different error now:
import torch
from kan import KAN

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

# Converting data to Torch tensor
train_input = torch.tensor(X_train.to_numpy(), dtype=torch.float32)
train_label = torch.tensor(y_train.to_numpy(), dtype=torch.long)
val_input = torch.tensor(X_val.to_numpy(), dtype=torch.float32)
val_label = torch.tensor(y_val.to_numpy(), dtype=torch.long)
test_input = torch.tensor(X_test.to_numpy(), dtype=torch.float32)
test_label = torch.tensor(y_test.to_numpy(), dtype=torch.long)
dataset = {
    'train_input': train_input,
    'train_label': train_label,
    'val_input': val_input,
    'val_label': val_label,
    'test_input': test_input,
    'test_label': test_label
}

# Create model
model = KAN(width=[11, 2], grid=10, k=2)

# Fuctions for getting accuracy scores while training
def train_acc():
    preds = torch.argmax(model(dataset['train_input']), dim=1)
    return torch.mean((preds == dataset['train_label']).float())

def test_acc():
    preds = torch.argmax(model(dataset['test_input']), dim=1)
    return torch.mean((preds == dataset['test_label']).float())

# KAN model training
results = model.fit({'train_input': train_input, 'train_label': train_label, 'test_input': val_input, 'test_label': val_label},
                      metrics=(train_acc, test_acc),
                      opt="LBFGS", steps=100, loss_fn=torch.nn.CrossEntropyLoss())

# Predictions of train val and test datasets
test_preds = torch.argmax(model.forward(test_input).detach(),dim=1)
test_labels = test_label

train_preds = torch.argmax(model.forward(train_input).detach(),dim=1)
train_labels = train_label

val_preds = torch.argmax(model.forward(val_input).detach(),dim=1)
val_labels = val_label

# Evaluate metrics

print("Train ACC:", accuracy_score(train_labels.numpy(), train_preds.numpy()))

print("Val ACC:", accuracy_score(val_labels.numpy(), val_preds.numpy()))

print("Test ACC:", accuracy_score(test_labels.numpy(), test_preds.numpy()))

# Plotting KAN network
model.plot(scale=10)

# Learning curve based on ACC and LOSS
plt.figure(figsize=(10, 5))
plt.plot(results["train_acc"], label='Training Accuracy')
plt.plot(results["test_acc"], label='Val Accuracy')
plt.plot(results["train_loss"], label='Training Loss')
plt.plot(results["test_loss"], label='Val Loss')
plt.title('Training and Val Accuracy over Iterations')
plt.xlabel('Iteration')
plt.ylabel('Accuracy & Loss')
plt.legend()
plt.grid(True)
plt.show()

`checkpoint directory created: ./model
saving model version 0.0
description:   0%|                                                          | 0/100 [00:00<?, ?it/s]
AssertionError                            Traceback (most recent call last)
 in <cell line: 41>()
39
40 # KAN model training
---> 41 results = model.fit({'train_input': train_input, 'train_label': train_label, 'test_input': val_input, 'test_label': val_label},
42                       metrics=(train_acc, test_acc),
43                       opt="LBFGS", steps=100, loss_fn=torch.nn.CrossEntropyLoss())
4 frames
/usr/local/lib/python3.10/dist-packages/kan/MultKAN.py in fit(self, dataset, opt, steps, log, lamb, lamb_l1, lamb_entropy, lamb_coef, lamb_coefdiff, update_grid, grid_update_num, loss_fn, lr, start_grid_update_step, stop_grid_update_step, batch, metrics, save_fig, in_vars, out_vars, beta, save_fig_freq, img_folder, singularity_avoiding, y_th, reg_metric, display_metrics)
944
945             if _ % grid_update_freq == 0 and _ < stop_grid_update_step and update_grid and _ >= start_grid_update_step:
--> 946                 self.update_grid(dataset['train_input'][train_id])
947
948             if opt == "LBFGS":
/usr/local/lib/python3.10/dist-packages/kan/MultKAN.py in update_grid(self, x)
356
357     def update_grid(self, x):
--> 358         self.update_grid_from_samples(x)
359
360     def initialize_grid_from_another_model(self, model, x):
/usr/local/lib/python3.10/dist-packages/kan/MultKAN.py in update_grid_from_samples(self, x)
352     def update_grid_from_samples(self, x):
353         for l in range(self.depth):
--> 354             self.get_act(x)
355             self.act_fun[l].update_grid_from_samples(self.acts[l])
356
/usr/local/lib/python3.10/dist-packages/kan/MultKAN.py in get_act(self, x)
1737         save_act = self.save_act
1738         self.save_act = True
-> 1739         self.forward(x)
1740         self.save_act = save_act
1741
/usr/local/lib/python3.10/dist-packages/kan/MultKAN.py in forward(self, x, singularity_avoiding, y_th)
365     def forward(self, x, singularity_avoiding=False, y_th=10.):
366
--> 367         assert x.shape[1] == self.width_in[0]
368
369         x = x[:,self.input_id.long()]
AssertionError:`

Also how would one go about doing feature selection with KAN?

Also how would one go about doing feature selection with KAN?

I have wrote code for feature selection, I will share it

Exciting! Where will you post it?

an example of how to do feature selection using KANs: https://github.com/KindXiaoming/pykan/blob/master/tutorials/Interp_4_feature_attribution.ipynb

It just doesn't like my dataset for some reason I've tried all kinds of things but i get these error - any idea why?:
from kan import *
import numpy as np

# Assuming X_train, y_train, X_val, y_val, X_test, y_test are already defined

# Combine training and validation sets
X_train_combined = np.concatenate((X_train, X_val), axis=0)
y_train_combined = np.concatenate((y_train, y_val), axis=0)

# Ensure no None values and correct shapes
assert X_train_combined is not None
assert y_train_combined is not None
assert X_test is not None
assert y_test is not None
assert X_train_combined.shape[0] == y_train_combined.shape[0]
assert X_test.shape[0] == y_test.shape[0]

# Convert y_train_combined and y_test to float, as sometimes integer arrays might cause issues
y_train_combined = y_train_combined.astype(float)
y_test = y_test.astype(float)

# Debugging: Print shapes and types of the dataset elements
print(f"train_input shape: {X_train_combined.shape}, dtype: {X_train_combined.dtype}")
print(f"train_output shape: {y_train_combined.shape}, dtype: {y_train_combined.dtype}")
print(f"test_input shape: {X_test.shape}, dtype: {X_test.dtype}")
print(f"test_output shape: {y_test.shape}, dtype: {y_test.dtype}")

# Create the dataset in the expected format
dataset = {
    'train_input': X_train_combined,
    'train_output': y_train_combined,
    'test_input': X_test,
    'test_output': y_test
}

# Verify the dataset structure
print(f"dataset keys: {dataset.keys()}")
for key in dataset:
    print(f"{key} shape: {dataset[key].shape}, dtype: {dataset[key].dtype}")

# Function to safely check if an array is None or empty
def safe_check_none(arr):
    return arr is None or (isinstance(arr, np.ndarray) and arr.size == 0)

# Check for None values explicitly before passing to model
if safe_check_none(dataset['train_input']) or safe_check_none(dataset['train_output']) or safe_check_none(dataset['test_input']) or safe_check_none(dataset['test_output']):
    raise ValueError("One of the dataset components is None or empty")

# Create and train the KAN model
model = KAN(width=[X_train_combined.shape[1], 10, 10, 1], seed=2)

# Explicitly define a function to handle the ambiguous truth value check
def custom_fit(model, dataset, *args, **kwargs):
    try:
        model.fit(dataset, *args, **kwargs)
    except ValueError as e:
        if "ambiguous truth value" in str(e):
            print("Handling ambiguous truth value check")
            model.fit(dataset, *args, **kwargs)
        else:
            raise e

# Use the custom fit function
custom_fit(model, dataset, steps=50, lamb=1e-3, reg_metric='edge_forward_n')

train_input shape: (2134, 3854), dtype: float64
train_output shape: (2134,), dtype: float64
test_input shape: (251, 3854), dtype: float64
test_output shape: (251,), dtype: float64
dataset keys: dict_keys(['train_input', 'train_output', 'test_input', 'test_output'])
train_input shape: (2134, 3854), dtype: float64
train_output shape: (2134,), dtype: float64
test_input shape: (251, 3854), dtype: float64
test_output shape: (251,), dtype: float64
checkpoint directory created: ./model
saving model version 0.0

description:   0%|                                                           | 0/50 [00:00<?, ?it/s]
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-36-8c207fd00f61>](https://localhost:8080/#) in <cell line: 64>()
     62
     63 # Use the custom fit function
---> 64 custom_fit(model, dataset, steps=50, lamb=1e-3, reg_metric='edge_forward_n')

5 frames
[<ipython-input-36-8c207fd00f61>](https://localhost:8080/#) in custom_fit(model, dataset, *args, **kwargs)
     59             model.fit(dataset, *args, **kwargs)
     60         else:
---> 61             raise e
     62
     63 # Use the custom fit function

[<ipython-input-36-8c207fd00f61>](https://localhost:8080/#) in custom_fit(model, dataset, *args, **kwargs)
     53 def custom_fit(model, dataset, *args, **kwargs):
     54     try:
---> 55         model.fit(dataset, *args, **kwargs)
     56     except ValueError as e:
     57         if "ambiguous truth value" in str(e):

[/usr/local/lib/python3.10/dist-packages/kan/MultKAN.py](https://localhost:8080/#) in fit(self, dataset, opt, steps, log, lamb, lamb_l1, lamb_entropy, lamb_coef, lamb_coefdiff, update_grid, grid_update_num, loss_fn, lr, start_grid_update_step, stop_grid_update_step, batch, metrics, save_fig, in_vars, out_vars, beta, save_fig_freq, img_folder, singularity_avoiding, y_th, reg_metric, display_metrics)
    944
    945             if _ % grid_update_freq == 0 and _ < stop_grid_update_step and update_grid and _ >= start_grid_update_step:
--> 946                 self.update_grid(dataset['train_input'][train_id])
    947
    948             if opt == "LBFGS":

[/usr/local/lib/python3.10/dist-packages/kan/MultKAN.py](https://localhost:8080/#) in update_grid(self, x)
    356
    357     def update_grid(self, x):
--> 358         self.update_grid_from_samples(x)
    359
    360     def initialize_grid_from_another_model(self, model, x):

[/usr/local/lib/python3.10/dist-packages/kan/MultKAN.py](https://localhost:8080/#) in update_grid_from_samples(self, x)
    352     def update_grid_from_samples(self, x):
    353         for l in range(self.depth):
--> 354             self.get_act(x)
    355             self.act_fun[l].update_grid_from_samples(self.acts[l])
    356

[/usr/local/lib/python3.10/dist-packages/kan/MultKAN.py](https://localhost:8080/#) in get_act(self, x)
   1730         if isinstance(x, dict):
   1731             x = x['train_input']
-> 1732         if x == None:
   1733             if self.cache_data != None:
   1734                 x = self.cache_data

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

@apavlo89  Here is my code for feature importance:
# Modeli oluşturma
model = KAN(width=[4,7,1], grid=3, k=11)

# Modeli eğitme
results = model.train({'train_input': train_input, 'train_label': train_label, 'test_input': val_input, 'test_label': val_label},
                      opt="LBFGS", steps=150, loss_fn=torch.nn.MSELoss())

# Validaionları verdik her ne kadar 'test_input': val_input yazsa da çünkü KAN model train dataset dict'ında test_input adlı bir dict istiyo

layer_1 = model.acts_scale[0].detach().numpy()
layer_2 = model.acts_scale[1].detach().numpy()

columns = list(X.columns)
importance_values = np.dot(layer_1.T, layer_2.T).flatten()

sorted_indices = np.argsort(importance_values)[::1]
sorted_importance_values = importance_values[sorted_indices]
sorted_columns = np.array(columns)[sorted_indices]

# Bar plot oluşturma
plt.figure(figsize=(12, 6))
plt.barh(sorted_columns, sorted_importance_values, color='skyblue')
plt.xlabel('Feature Importance')
plt.ylabel('Features')
plt.title('Feature Importance of KAN')
plt.show()

You will see the barh plot

Hi @apavlo89 , looks like your data are numpy array, could you please try convert them to torch tensors?

import torch
from kan import KAN

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

# Check the shape of your data
print(f"Shape of X_train: {X_train.shape}")
print(f"Shape of X_val: {X_val.shape}")
print(f"Shape of X_test: {X_test.shape}")

# Converting data to NumPy arrays
X_train_np = X_train.to_numpy()
y_train_np = y_train.to_numpy()
X_val_np = X_val.to_numpy()
y_val_np = y_val.to_numpy()
X_test_np = X_test.to_numpy()
y_test_np = y_test.to_numpy()

# Converting data to Torch tensor
train_input = torch.tensor(X_train_np, dtype=torch.float32)
train_label = torch.tensor(y_train_np, dtype=torch.long)
val_input = torch.tensor(X_val_np, dtype=torch.float32)
val_label = torch.tensor(y_val_np, dtype=torch.long)
test_input = torch.tensor(X_test_np, dtype=torch.float32)
test_label = torch.tensor(y_test_np, dtype=torch.long)

dataset = {
    'train_input': train_input,
    'train_label': train_label,
    'val_input': val_input,
    'val_label': val_label,
    'test_input': test_input,
    'test_label': test_label
}

# Ensure the input width matches the number of features
input_width = X_train_np.shape[1]

# Create model
model = KAN(width=[input_width, 2], grid=10, k=2)

# Workaround for the issue by setting save_act to False
model.save_act = False

# Functions for getting accuracy scores while training
def train_acc():
    preds = torch.argmax(model(dataset['train_input']), dim=1)
    return torch.mean((preds == dataset['train_label']).float())

def test_acc():
    preds = torch.argmax(model(dataset['test_input']), dim=1)
    return torch.mean((preds == dataset['test_label']).float())

# KAN model training using the fit method
results = model.fit({'train_input': train_input, 'train_label': train_label, 'test_input': val_input, 'test_label': val_label},
                    opt="LBFGS", steps=150, loss_fn=torch.nn.CrossEntropyLoss())

layer_1 = model.acts_scale[0].detach().numpy()
layer_2 = model.acts_scale[1].detach().numpy()

columns = list(X_train.columns)
importance_values = np.dot(layer_1.T, layer_2.T).flatten()

sorted_indices = np.argsort(importance_values)[::1]
sorted_importance_values = importance_values[sorted_indices]
sorted_columns = np.array(columns)[sorted_indices]

# Create the bar plot
plt.figure(figsize=(12, 6))
plt.barh(sorted_columns, sorted_importance_values, color='skyblue')
plt.xlabel('Feature Importance')
plt.ylabel('Features')
plt.title('Feature Importance of KAN')
plt.show()

Output and error :/ Dreaming of the day someone makes an easy to use autoKAN package.. W

Shape of X_train: (1886, 171)
Shape of X_val: (251, 171)
Shape of X_test: (253, 171)
checkpoint directory created: ./model
saving model version 0.0
| train_loss: nan | test_loss: nan | reg: 0.00e+00 | : 100%|██████| 150/150 [01:42<00:00,  1.46it/s]saving model version 0.1

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
[<ipython-input-12-674e38bca30b>](https://localhost:8080/#) in <cell line: 63>()
     61                     opt="LBFGS", steps=150, loss_fn=torch.nn.CrossEntropyLoss())
     62
---> 63 layer_1 = model.acts_scale[0].detach().numpy()
     64 layer_2 = model.acts_scale[1].detach().numpy()
     65

IndexError: list index out of range

try print(model.acts_scale), is it None? If yes, what is model.save_act (True or False)? If you try a dataset which does not return nan loss, does this problem persist?

If i remove the feature selection code then it runs just fine. the issue appears in the feature selection part:

import torch
from kan import KAN

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

# Check the shape of your data
print(f"Shape of X_train: {X_train.shape}")
print(f"Shape of X_val: {X_val.shape}")
print(f"Shape of X_test: {X_test.shape}")

# Converting data to NumPy arrays
X_train_np = X_train.to_numpy()
y_train_np = y_train.to_numpy()
X_val_np = X_val.to_numpy()
y_val_np = y_val.to_numpy()
X_test_np = X_test.to_numpy()
y_test_np = y_test.to_numpy()

# Converting data to Torch tensor
train_input = torch.tensor(X_train_np, dtype=torch.float32)
train_label = torch.tensor(y_train_np, dtype=torch.long)
val_input = torch.tensor(X_val_np, dtype=torch.float32)
val_label = torch.tensor(y_val_np, dtype=torch.long)
test_input = torch.tensor(X_test_np, dtype=torch.float32)
test_label = torch.tensor(y_test_np, dtype=torch.long)

dataset = {
    'train_input': train_input,
    'train_label': train_label,
    'val_input': val_input,
    'val_label': val_label,
    'test_input': test_input,
    'test_label': test_label
}

# Ensure the input width matches the number of features
input_width = X_train_np.shape[1]

# Create model
model = KAN(width=[input_width, 2], grid=4, k=1)

# Workaround for the issue by setting save_act to False
model.save_act = False

# Functions for getting accuracy scores while training
def train_acc():
    preds = torch.argmax(model(dataset['train_input']), dim=1)
    return torch.mean((preds == dataset['train_label']).float())

def test_acc():
    preds = torch.argmax(model(dataset['test_input']), dim=1)
    return torch.mean((preds == dataset['test_label']).float())

# KAN model training using the fit method
results = model.fit({'train_input': train_input, 'train_label': train_label, 'test_input': val_input, 'test_label': val_label},
                    opt="LBFGS", steps=100, loss_fn=torch.nn.CrossEntropyLoss())
#opt can be Adam or LBFGS
print(model.acts_scale)
#####################################feature selection###########################################
layer_1 = model.acts_scale[0].detach().numpy()
layer_2 = model.acts_scale[1].detach().numpy()

columns = list(X_train.columns)
importance_values = np.dot(layer_1.T, layer_2.T).flatten()

sorted_indices = np.argsort(importance_values)[::1]
sorted_importance_values = importance_values[sorted_indices]
sorted_columns = np.array(columns)[sorted_indices]

## Create the bar plot
plt.figure(figsize=(12, 6))
plt.barh(sorted_columns, sorted_importance_values, color='skyblue')
plt.xlabel('Feature Importance')
plt.ylabel('Features')
plt.title('Feature Importance of KAN')
plt.show()
##################################################################################################

# Predictions of train val and test datasets
test_preds = torch.argmax(model.forward(test_input).detach(),dim=1)
test_labels = test_label

train_preds = torch.argmax(model.forward(train_input).detach(),dim=1)
train_labels = train_label

val_preds = torch.argmax(model.forward(val_input).detach(),dim=1)
val_labels = val_label

# Evaluate metrics

print("Train ACC:", accuracy_score(train_labels.numpy(), train_preds.numpy()))

print("Val ACC:", accuracy_score(val_labels.numpy(), val_preds.numpy()))

print("Test ACC:", accuracy_score(test_labels.numpy(), test_preds.numpy()))

# Plotting KAN network
#model.plot(scale=input_width)
print(results)

# Learning curve based on ACC and LOSS
plt.figure(figsize=(10, 5))
plt.plot(results["train_acc"], label='Training Accuracy')
plt.plot(results["test_acc"], label='Val Accuracy')
plt.plot(results["train_loss"], label='Training Loss')
plt.plot(results["test_loss"], label='Val Loss')
plt.title('Training and Val Accuracy over Iterations')
plt.xlabel('Iteration')
plt.ylabel('Accuracy & Loss')
plt.legend()
plt.grid(True)
plt.show()

`Shape of X_train: (1886, 171)
Shape of X_val: (251, 171)
Shape of X_test: (253, 171)
checkpoint directory created: ./model
saving model version 0.0
| train_loss: 6.57e-01 | test_loss: 9.09e-01 | reg: 0.00e+00 | : 100%|█| 100/100 [01:40<00:00,  1.01saving model version 0.1
[]

IndexError                                Traceback (most recent call last)
 in <cell line: 65>()
63 print(model.acts_scale)
64 #####################################feature selection###########################################
---> 65 layer_1 = model.acts_scale[0].detach().numpy()
66 layer_2 = model.acts_scale[1].detach().numpy()
67
IndexError: list index out of range`

repo: KindXiaoming/pykan


text:
feat(hk sources): add function for fetching hk sources

This PR adds new function for fetching the sources that have been used to save HealthKit samples:

query sources with HKCategoryTypeIdentifier or HKQuantityTypeIdentifier
new querySources function for fetching sources, returns a list of source objects that contain name and bundleId of the source
new useSources hook
new sources section in the test app with examples on fetching both  HKCategoryTypeIdentifier and HKQuantityTypeIdentifier sources

Looks great, thanks for contributing @plahteenlahti 👍

Hey, @robertherber, is possible for you to make a new release with this change included? Would like to test it in a production app, thanks ☺

@plahteenlahti It's out with 5.4.0 :)

source: gharchive/pull-request

authors: plahteenlahti robertherber

repo: Kingstinct/react-native-healthkit

license: MIT

license_type: permissive

license_source: github-api


text:
Question about the paper

The author claims in their paper that their method is unsupervised method. Actually, as in stated in the paper, the author use clean GT as supervision. if I miss something, could you explain it?

We define our approach as an unsupervised domain adaption  salient object detection methods, but not an unsupervised method. Only when comparing with others, we follow the definition of existing deep USOD methods[1,2] that defines "unsupervised learning" as learning without human annotations. Most of them refer to models trained with noise labels generated by traditional methods.
References
[1] Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective, CVPR 2018
[2] DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision, NeurIPS 2019

Thanks for your reply!
I stand with your response. There are some misleading sentence in the conclusion----"In this paper, we propose to tackle deep unsupervised salient object detection from a novel perspective, i.e., learning from synthetic but clean labels"

Thanks for pointing it out. Actually, we want to express that our approach learns from synthetic but clean labels instead of  noise labels generated by traditional methods like existing methods. Sorry for the misleading and we will add this question to FAQ section for better explanation.

您好，请问下合成图像的标签是怎么得到的？

@wangjinkai8611 网上搜集的带有透明背景的素材图片的Alpha通道

source: gharchive/issue

authors: Kinpzz wangjinkai8611 wuzhenyubuaa

repo: Kinpzz/UDASOD-UPL


text:
Summarize results from cluster

summarize_results can now fetch results from cluster

Merged a modified version in #111

authors: Kipok i-vainn

repo: Kipok/NeMo-Skills


text:
Footer Display Issue on Exchanges Page

On Exchanges Page - The Footer Appears at the Top Instead of the Bottom.

Hey, I would like to take this up

Go Ahead, @IrfanSalim!


text:
Add matchesRegex to helper functions

Hi @Kirlovon,
Would you be willing to accept a PR to include a matchesRegex helper function in lib/helpers.ts?
I am thinking something like the below:
/**
 * Selects documents where the value of a field matches a regular expression.
 * @param value
 */
export function matchesRegex(expression: RegExp) {
	return (target: Readonly<DocumentValue>) => isString(target) && expression.test(target);
}

With a use case being something like this:
const regExp = new RegExp("/foo/g");

db.findMany({ someField: matchesRegex(regExp) });

If you are willing to accept this I will write a PR to add this feature.
Thanks for all of your work on this project so far. I have really enjoyed using it.

Closing. Regex is already supported. See #5.

repo: Kirlovon/AloeDB


text:
TreeMap to sort mapValue

The test case com.networknt.audit.AuditHandlerTest.testAuditWithErrorStatus fails due to the below assertion
https://github.com/KiruthikaJanakiraman/light-4j/blob/176eb7f338294c483b6c557ca2ef7911730129bd/audit/src/test/java/com/networknt/audit/AuditHandlerTest.java#L172
The assert statement fetches Status from mapValue which returns a Map. The order of the Map is not deterministic which may cause the assertion to fail when comparing it to the given Status string.
This PR fixes the test by converting the Map returned by mapValue.get("Status") to a TreeMap with sorts the fields within the Map.
The following command can be used to reproduce assertion failures and verify the fix:
mvn -pl edu.illinois:nondex-maven-plugin:2.1.1:nondex audit -Dtest=com.networknt.audit.AuditHandlerTest.testAuditWithErrorStatus

Test Environment:
Java version "1.8.0_381"
Apache Maven 3.6.3
macOS Venture Version 13.4.1 (22F82)

Please let me know if you have any concerns or questions.

Nice fix, I like the way you dealt with the JSON assertion problem without adding and using a new dependency!
I would recommend adding the full path of the test, like com.networknt.audit...Test#testcase so it is easier for the owner of the repo to find the exact test you are referencing.
Maybe you want to add something like "fix flaky test fullpath" in the headline/subject of the PR, so it is clear what the goal of this PR is.

Nice fix, I like the way you dealt with the JSON assertion problem without adding and using a new dependency! I would recommend adding the full path of the test, like com.networknt.audit...Test#testcase so it is easier for the owner of the repo to find the exact test you are referencing. Maybe you want to add something like "fix flaky test fullpath" in the headline/subject of the PR, so it is clear what the goal of this PR is.

Sure, thanks for the review. I have updated the title as suggested.

Your fix looks good, it is a typical flaky test that we talked about in class. Also, the reproduction code is really helpful.

Additionally, I would recommend to clearly separate the pull request in 3 parts (maybe using headlines):

Problem
Solution and
Reproduce

authors: Einsteinnnnn KiruthikaJanakiraman hofi1


text:
[BREAKING CHANGE] Create new Select with react-select@3

https://react-select.com/
Vous pouvez lire le README pour voir les choses qui ont évoluées.
Je me suis basé sur la nouvelle charte graphique https://app.abstract.com/projects/67e5bf40-6a62-11e9-ab92-01fc018ad3a7/branches/5dadd0c8-ffc2-4ba4-8a1c-a56ed3337c66/commits/4fc8e60559c426a0ed84c9a9e02cb3977c4b2b37/files/D681D1DD-7F19-4397-954C-78509DA47F6A/layers/FF666C24-80AF-4063-A333-96D91E1CC3D9 pour les styles.
Je vais faire des tests sur KissKiss pour voir le composant n'a pas de regressions.

Au niveau navigation clavier, ça marche bien chez toi ?

@joachimesque, non effectivement j'avais introduit une régressions sur l'a11y. Ça devrait être mieux maintenant !

@joachimesque avec tes devs on peut close cette PR ?

@joachimesque avec tes devs on peut close cette PR ?

@FlorentD oui je pense, il faudra faire une transition douce vers le nouveau DropdownSelect mais ça devrait être nickel !

authors: FlorentD joachimesque

repo: KissKissBankBank/kitten

license: mit

license_source: bigquery


text:
No indication of how to save, or that saving is automatic, or to prevent saving.

ZMA does not tell the user saving is automatic.
There are users who may also make changes but not wish to save those changes. What can they do?
What about users that want to save the project under another name?
I was genuinely confused if closing ZMA was going make me lose my KCL.

this is a good point because I often compare the behavior of our app to vscode, there are lots of similarities:

local files
some extensions in vscode call out to a cloud but all files are still local

but a big difference would be in vscode you inheritly hit save, in that regard it is pretty unintuitive, maybe something for the onboarding?
adding @franknoirot

Yeah this is one of the points along which I think our app borrows from Figma not VS Code. I propose two things:

Adding early in the onboarding that saving is automatic (we should also mention that they are saved locally)
Add a little toast like Figma used to show when users hit ⌘S or Ctrl+S that says "All your changes are auto-saved"

Here are some notes from Figma on their autosave feature: https://www.figma.com/blog/behind-the-feature-autosave/

authors: franknoirot jessfraz lf94

repo: KittyCAD/modeling-app


text:
Performance issue due to focus state in Safari

Not so much of an issue that needs fixing but it might be useful to mention in the docs:
While implementing a11y-dialog in a website I ran into a strange issue due to Safari’s user agent stylesheet. This is a fullscreen dialog, the dialog content spans the whole screen. As soon as the dialog opens focus is set on the dialog container. Safari applies user agent styles for the focus which then hinder the performance. For instance resizing the browser becomes very choppy. Also when you’ve applied animations to the dialog these don’t run and skip to the end immediately as Safari is struggling. The solution I’ve used to resolve this is to set .container:focus { outline: none}.

Hello, and thank you for opening an issue. I was wondering if you could create a small demo on CodeSandbox so I can play with it and try to diagnose it?
Thank you. 💖

I will do that!

I’ve set up a demo on codebase. While doing this I discovered a difference: in the Nextjs app that I’ve implemented the dialog in tabindex="-1" gets added to the container element but in the codebase demo this doesn’t happen. In the demo when opening the dialog the container element also doesn’t get focused like in the Nextjs app. I’m not sure why this difference occurs. Would you have an idea why this difference happens?
In any case in the demo app in Safari:

Open the dialog
Select the dialog container in the inspector
Check the :focus checkbox (see screenshot)
Resizing is choppy compared to the :focus being unchecked
Attempt resizing the window (while doing this you can also observe the cpu usage with Activity Monitor)

Hello! 👋
Brilliant catch, nicely done. Could you please test again with https://github.com/KittyGiraudel/react-a11y-dialog/releases/tag/6.1.4? I just published a fix that adds tabindex="-1". This was missing in the React port.

I’ve updated the codesandbox with v6.1.4: https://dbzvyy.csb.app When you now open the dialog in Safari and try to resize the window you should be able to observe the choppiness, especially when on a larger screen (also observe CPU usage with Activity Monitor.
Using the inspector add outline: none !important; to the dialog container element and try resizing the window again. The resizing should be smooth and CPU usage should be normal.
Again, I don’t think this needs a code change but it could use a mention somewhere in the docs, perhaps under styling.

I agree, with that. These are the styles that Safari adds:
:-internal-direct-focus {
  outline-color: -webkit-focus-ring-color;
  outline-style: auto;
  outline-width: 5px;
}

Perhaps the main culprit is -webkit-focus-ring-color and a solid color will work better.

I can confirm that redefining the outline like outline: 3px solid red; for instance also works fine.

Fascinating. I’ll try to confirm that on my end to see if you’re onto something. Can you share the exact Safari version (+ OS) so we compare our setups?

I’ve tested this in Safari Version 15.3 (17612.4.9.1.8)

So I’ve played a little bit with it and cannot seem to notice such a performance drain. Is there any chance you could create a short recording maybe? I might be doing something wrong with the reproduction. 😅

I’ll update the example with some animation (this is when I first noticed the issues with Safari) as it might demonstrate the issue a bit clearer.

Unfortunately I haven’t had the time to adjust the demo, I’m fine with closing the issue. Thanks for your time in any case 🙏🏻

authors: KittyGiraudel iksi


text:
Fixed bad include in FindProtobuf.cmake

Line 243 which includes FindPackageHandleStandardArgs fails in Windows under CMake 3.1.0.  Removing the directory prefix and file extension fixed it.  Confirmed working in Windows and Ubuntu 14.04.1.

All find modules include FPHSA that way.  What is the actual error you see?

Sure but by specifying the list directory isn't necessary.  This was a specific case where I found the problem when specifying a different CMAKE_MODULE_PATH.  FindProtobuf.cmake was not included with my CMake so I pulled it into my project.  This change makes the module work anywhere instead of just in the default directory.  Perhaps it's worth changing in others as well.

The specific error is that it can't find the file C:/path/to/project/cmake/module/dir/FindPackageHandleStandardArgs.cmake

So it works within CMake but not when copying the module out.  This is expected.  If you copy the module out you can edit this line, but the copy within CMake wants to make sure it gets the FPHSA it expects.  In the past we did not do this and ended up having to create CMP0017 to protect against out-of-date copies of CMake builtin modules in the project CMAKE_MODULE_PATH.
Note when copying a module there the copyright notice has instructions below it about what to do with the license.

The module was not included to begin with in CMake for Windows 3.1.0.  Is there some way I can get this the "proper" way?

Huh? FindProtobuf has been distributed with CMake since 2.8.0.  I see it in my 3.1 installation.

Sheesh.  I do not know what happened here, I do see it now.  I have no idea why I was having the issue but I can't seem to recreate it.  My apologies.

repo: Kitware/CMake


text:
Importing history from gaia

As discussed with @aashish24, this is a subtree import of the Gaia python package so we can hack on them in one place.

LGTM, let the fun begin!

:dancers:

:clap:

authors: aashish24 jbeezley jeffbaumes

repo: Kitware/romanesco


text:
Modularité du résultat de quicksearch

Bonjour,
Serait-il possible de rendre modulaire le composant result du quicksearch (I.E. qu'il soit possible de lui donner un result custom en props.) ?
Merci =).

+1

:gb: :gb: :gb:

Here's the english version:
Would it be possible to customize the quicksearch component by giving a custom result using props?

Tu peux récupérer quickSearch en tant que mixin, et override la méthode _renderResults, ça te laissera le loisir de faire ce que tu veux

Propose a PR
https://github.com/KleeGroup/focus-components/blob/develop/src/page/search/quick-search/index.js#L156

@Ephrame , any chance to have a Pull Request?

@Ephrame : Any PR ?

I know, but the delivery have taken all my time.

it has be done

authors: Bernardstanislas Ephrame TomGallon Tommass Tsj- pierr

repo: KleeGroup/focus-components


text:
🛑 AWX is down

In d6ce5c7, AWX (http://awx.klutzybubbles.me) was down:

HTTP code: 0
Response time: 0 ms

Resolved: AWX is back up in 4e270f6 after 4 hours, 58 minutes.

repo: KlutzyBubbles/klutzybubbles-status


text:
Updated to new working version Calamine

Added the "Get-Ipv4SbubnetFromHostCount" function which replaced the "Get-CIDRFromHostCount" function.  It added the subnet and binary for a more robust output.
Updated Readme

Publishing a new version: Calamine


text:
Wrong ClassMetadata

Trying use the bundle in SF5 I obtain dtin error in some behaviors
!!
!!    Argument 1 passed to Knp\DoctrineBehaviors\ORM\Blameable\BlameableSubscribe
!!    r::isBlameable() must be an instance of Doctrine\Common\Persistence\Mapping
!!    \ClassMetadata, instance of Doctrine\ORM\Mapping\ClassMetadata given, calle
!!    d in /Users/ephraim/Develop/Oltremare/OltremareWeb/vendor/knplabs/doctrine-
!!    behaviors/src/ORM/Blameable/BlameableSubscriber.php on line 76
!!
In my project I have
        "name": "doctrine/common",
        "version": "3.1.2",

        "name": "doctrine/dbal",
        "version": "2.13.4",

        "name": "doctrine/doctrine-bundle",
        "version": "2.4.3",

        "name": "doctrine/orm",
        "version": "2.10.1",

I solve in my local replacing
use Doctrine\Common\Persistence\Mapping\ClassMetadata,

with
use Doctrine\ORM\Mapping\ClassMetadata,

Closing for lack of feedback. We're narrowing issues to keep focus on active and engaging contributor to keep project growing. Thank you for understanding.
Feel free to re-open with failing test case.

Closing for lack of feedback. We're narrowing issues to keep focus on active and engaging contributor to keep project growing. Thank you for understanding.
Feel free to re-open with failing test case.

authors: TomasVotruba ephp

repo: KnpLabs/DoctrineBehaviors


text:
submodules error

needs to modify .gitmodules file
origin url is 	url = git@github.com:shysaur/Fragaria.git
new url is url=https://github.com/shysaur/Fragaria.git

It should work fine with the git URL as well, have you added an SSH key to Github?

repo: KnuffApp/Knuff


text:
Ineffective for Sonic & All-Stars Racing Transformed

Hello. Thank you for your great work. I have seen this work for the XInputTest.exe program that can be found in the x360ce repository but unfortunately it seems ineffective for the one game I'm trying to play. I also tried your new and wine branches but they didn't help.
To cut a long story short, without XInput it's like one of the axis is permanently stuck, even under real Windows 10. x360ce works under Windows but it also has no effect under Wine for this particular game. I tried to get more information from the x360ce team in order to approach the Wine team but despite giving some hints to another user earlier this year, they decided they weren't going to help me today. I'd rather use your solution anyway.
This game requires x360ce's COM HookMask. After comparing your code against theirs, it looks as though you're doing something similar but not hooking as many functions. Perhaps CoGetClassObject is significant? I captured logging from starting Steam, starting the game, quitting the game, and then quitting Steam. I can see CoGetClassObject mentioned just before it outputs controller messages at line 1081 that you only see when it has fallen back to DirectInput.
One more thing, when using this project with Launcher.exe, the game's configurator, it crashes out on startup with a StackOverflowException after printing this over and over.
koku-xinput-wine: CoSetProxyBlanket(...);

This doesn't matter much as you generally don't need to use the configurator and this doesn't happen to the game itself. Just thought I should make you aware of it.
Please let me know if you need any more information.

The whole COM thing is pretty ugly and stupid...
With https://github.com/KoKuToru/koku-xinput-wine/pull/9 I started to cleanup some stuff..
But the COM-Part is still pretty ugly. It's maybe much easier to add the "gamepads" to the wine devices..
I tried to find the code where the gamepad gets returned but couldn't find it..
The COM-Hack does nothing else then changing the Name/ID (or whatever) so the game thinks it's a XInput Controller.
Maybe I have time this holidays (~20.December) to check the wine code again.. if I don't forget..

Thanks for the quick reply. I know that Wine's DirectInput code adds the devices here for js and here for event but I suspect that's not the code you meant. xboxdrv allows me to name the device whatever I like but if I try VID_3ED9&PID_9E57&IG_00, it doesn't make any difference, probably because this is only exposed through DirectInput. You're talking about WMI?

well I am searching for the code which returns the PnP devices.. (and/or register the devices)
the thing I hook

Maybe I have time this holidays (~20.December) to check the wine code again.. if I don't forget..

So about that code... :wink: In your own time, man, have a good Christmas.

The alternative project, wine-xinput, explains that this game doesn't work because it mixes DirectInput with XInput. I have heard that Wine's own XInput support is slowly progressing so hopefully something can be done to make this work.

The same issue seem to affect Pro Evolution Soccer 2016/2017 as well, for the same reason. It does DInput enumeration before XInput, the same as Sonic ASRT.

Sonic Racing Transformed now works perfectly, although you can't load the Launcher (the config program) if Koku is loaded as it'll crash.
On the other hand, Pro Evo 2017 (and its config program) crash if you load Koku:
=>0 0xf7f7b763 _ZN4koku16CoCreateInstanceERK5_GUIDP8IUnknownjS2_PPv+0xb3() in koku-xinput-wine.so (0x0032f328)

Another one is Castle Crashers. With Koku loaded it sits at 100% CPU usage and never actually loads the game. Loads fine without Koku (but obviously the controllers then don't work).

Sonic Racing Transformed now works perfectly. Its config program (Launcher.exe) will crash if Koku is loaded, but you don't need to use that program when using XInput anyway.

I found this hard to believe given how much trouble I'd had with it before but you are… almost right! It's not immediately apparent but I'm quite sure that you don't get full acceleration. When using the keyboard, I came 4th without trying that hard. When using the controller, I struggled to keep out of last place and the car felt a bit sluggish. I'm not sure why as XInputTest.exe shows what I believe is the full range of values for the trigger, 0-255. I usually use Thrustmaster controllers with xboxdrv. I tried to use with a real 360 controller but the wireless adapter wasn't playing ball today.

Odd, I came second in B class after having not played for probably more than a year and repeatedly cocking up, so I wonder if that's not a universal problem.
Worth noting that Castle Crashers works with one of the DLL based XInput emulators so it's not universally broken.

Here's an update for Wine 3.5. As you know, we can't build this project any more. If I run the game with xboxdrv but without any additional hacks, it seems to use XInput (the button icons are green A, red B, etc) and the menus work but I can't accelerate or reverse. I guess Wine didn't put it the extra stuff needed to make this one work yet.

authors: Enverex KoKuToru chewi

repo: KoKuToru/koku-xinput-wine


text:
Add LControl component that allows custom Leaflet Controls to be built in Vue

Provides a resolution to #142 by adding a Vue component whose content is wrapped into a custom Leaflet Control.
Super minimal example:
<l-map :zoom="zoom" :center="center">
  <l-control>
    Control content
  </l-control>
</l-map>

Also includes a new example page demonstrating the usage, "Custom Leaflet Control".

When this is gonna be merged? I need this.

@mikeu Thanks for the great work!
@justinbeatz will try to merge it by the end of the month.
Mickaël

I ended up copying the control component into my project and it worked great! I hope this gets merged in soon.

authors: KoRiGaN justinbeatz mikeu wishinghand


text:
Stream output of cargo

Currently, cargo-pgo runs cargo, gathers its output and then writes it to the output. However, it would be much nicer if we could instead stream the output of cargo interactively, so that the user can see it immediately and not just at the end.
Use-case: https://github.com/Kobzol/cargo-pgo/issues/5#issuecomment-1265031077

BTW - if possible, try to propagate color formatting as well. I guess cargo disables color automatically when it detects its output is redirected, so it might involve specifying some options.

When I do a cargo build after a cargo pgo it causes a full dependency rebuild.
I'm not sure if my "config.toml" rustflags are being honoured?
I have them set as ["-C", "target-cpu=native", "-Z", "tune-cpu=native"]
I did a RUSTFLAGS="-C target-cpu=native -Z tune-cpu=native" cargo pgo optimize and it did seem to produce a different binary, but still did a full dependency rebuild when running cargo build.
Will this change allow the verbose cargo output to be streamed as well? So I can see what flags are being set during the build.
Great tool by the way 👍

Rustflags from the config file should be honored, but cargo build will always do a rebuild AFAIK, because cargo pgo uses PGO flags (instrumentation/optimization) that will invalidate other builds. You would have to pass e.g. RUSTFLAGS="-Cprofile-generate" to avoid a rebuild, but then you'd just use PGO again.. :)
Yes, with the streaming change, verbose output is also streamed and printed:
$ cargo pgo build -- --verbose

I published version 0.2.2 with cargo output streaming.

authors: Kobzol pkolaczk zeroflaw

repo: Kobzol/cargo-pgo


text:
Incorrect javascript paths

The generated static website assumes that all generated HTML files are located in the root of src/, which makes the ../assets links in the HTMLs incorrect if this assumption does not hold.

Hi, the tool should generate all source files in the src directory (apart from index.html and remarks.html). Did it generate files in some nested directory, or elsewhere?

The tool nested the files, src/main.rs's results were placed into src/src/main.rs.html. I am wondering if the library results got scattered around my filesystem now :D
Here's how the folder structure looks like:

I should add that I'm trying this on Windows, so I have a bunch of src/C:\Users\bugad\.cargo\registry paths, too, but that is somewhat of a different issue

Sorry for that, this crate is not well tested on Windows, and I already saw several similar issues. Are you using the newest version of the crate?
I'm on a vacation now, I'll take a look at it at the beginning of September.

I've just installed yesterday, so probably the latest, yes.
No worries, enjoy your vacation!

Can you please check if this branch fixes your issue?
$ cargo install --git https://github.com/Kobzol/cargo-remark --branch path-normalization

Thanks, it seems this particular issue is indeed fixed using that branch.

authors: Kobzol bugadani

repo: Kobzol/cargo-remark


text:
Readme aanvullen

De basis voor de readme is er wel al, dus je hoeft hem alleen nog maar verder in te vullen

Aangevuld

authors: KoenHaagsma zainuwachtig

repo: KoenHaagsma/Food-Finder


text:
Add execution of ImageTransformProcess in ImageLoading PipelineStep

We're just doing image ordering in ImageLoading pipeline step. Implementation of executing the given ImageTransformProcess along with it is missing here

Fixed here #57

repo: KonduitAI/konduit-serving


text:
[kong] add read-only root filesystem option

What this PR does / why we need it:
Adds an option to control enforcing read-only root filesystem in the PodSecurityPolicy.
Special notes for your reviewer:
Prior work in https://github.com/helm/charts/pull/19663

The issue where Enterprise wrote to root regardless of its prefix will be addressed in 1.5 (see FTI-1163 for internal discussion). Manual testing of upgrades and fresh installs of 1.5 preview builds confirmed that the issue is clear.
The second issue with the container image no longer appears to be present. I was not able to reproduce it with either core 1.5, core 2.0, or Enterprise 1.5.
The Postgres sub-chart does not play well with PSP automation, and requires creating a PSP independent of the chart (Bitnami does not provide a stock policy). It cannot reuse the ServiceAccount we create for Kong, as it both requires a writeable root filesystem and access to PVCs.

Checklist
[Place an '[x]' (no spaces) in all applicable fields. Please remove unrelated fields.]

[x] PR is based off the current tip of the next branch and targets next, not master
[x] New or modified sections of values.yaml are documented in the README.md
[x] Title of the PR and commit headers start with chart name (e.g. [kong])

Discussed offline with Travis that the blast radius here is very small and we can take a chance with RO Root FS to be true.

authors: hbagdi rainest

repo: Kong/charts


text:
Update Logging reference with details from Kong Nation post

@thibaultcha shared some helpful details on Kong's log levels and how one might utilize them in various circumstances https://discuss.konghq.com/t/production-kong-log-level/1949/5 - it'd be ideal if those details were added to Kong's logging reference https://docs.konghq.com/0.14.x/logging/ , for the benefit of future visitors to https://docs.konghq.com/

Hi @coopr :wave:
I would like to solve this issue as a part of hacktoberfest

Ok, give it a shot! It'll require a decent understanding of the subject matter, and some thoughtful writing :)


text:
docs(hub) x credential username deprecation

X-Credential-Identifier is the new header name.
Note: in reviewing, please make sure to review index.md files, not the old plugin versions (e.g., 2.1-x in most plugins).

rebased to solve conflicts


text:
Duplicate environment variables make UI unusable

Expected Behavior

Add duplicate environment names to different environments
A warning can be shown, but you can still use the UI

Actual Behavior

The project file with a duplicate cannot opened.
An error is displayed, but with no recourse
The error is "Application Error", "Failed to render." "Please send the following error to support@insomnia.rest." "Can't insert key env_92c7fbb142dae52b245b193894f0c2bc2645be0c, it violates the unique constraint" "Try to reload the app"

Reproduction Steps

Add duplicate environment names to different environments

Is there an existing issue for this?

[X] I have searched the issue tracker for this problem.

Additional Information
No response
Insomnia Version
2022.7.0
What operating system are you using?
Windows
Operating System Version
Windows 10 Version 22H2 OS build 19045.2364
Installation method
Download
Last Known Working Insomnia version
No response

Same with following version:
Version: Insomnia 2022.7.0
Build date: 12/15/2022
OS: Darwin arm64 22.2.0
Electron: 22.0.0
Node: 16.17.1
V8: 10.8.168.20-electron.0
Architecture: arm64

Maybe it's the same issue that occurs if you export a collection an try to import it. This ends in Can't insert key env_02f2448da27690815b8e7e0dedf62a96342e3dc6, it violates the unique constraint

authors: VolodymyrBiryuk awestphal jcbeck37

repo: Kong/insomnia


text:
Allow specifying multiple config files

Summary
We are using Kong in Kubernetes as a Gateway rather than an ingress, as we need the route functionality as part of the gateway (Timeouts & rate limits are increases per route).
We have an automated process which registers routes from an OpenAPI spec, and the configuration for services in yaml can get fairly large.
We are storing these yaml file as config maps, but Kubernetes has limits on how big the config maps can be.
We thought we could possibly specify multiple yaml files for config, but not currently supported.
Could be a great way for distributed teams to specify their own yaml files as well, without a big step in CI to generate a merged yaml file, and could be more declarative per team environment.

+1, this would be very nice. Providing a config dir path allows for multiple teams to drop-in configs. Handling conflict might be a bit tricky, but there’s value there. It would also help in certain config management scenarios, eg chef/puppet/ansible drops in context-specific configs (eg a proxy.conf, admin.conf, db.conf, etc).

Did some thinking around this, and providing a config dir path would be good, although will need to allow for recursive directories as well, as we will be specifying multiple configmaps for mounting inside the container, and these will probably be mounted on different sub paths.
# Example of mounting inside of kubernetes with configmaps
kong-configs/
├── service-x
│   └── kong.yml # Specified in configmap by service team
└── service-y
    └── kong.yml # Specified in configmap by service team

When the reloading or specifying configs to Kong, we would just pass the kong-configs dir as config path and it would need to find these files.
But an initContainer on Kong would also serve the same purpose, which makes the possible solution simpler.
Could be as simple as a cli command to merge configs and validate them and fail fast on conflict errors.

repo: Kong/kong


text:
Fix spelling in lua and t files

Summary

Fixes some typos
Checklist

[ ] The Pull Request has tests
[ ] There's an entry in the CHANGELOG
[ ] There is a user-facing docs PR against https://github.com/Kong/docs.konghq.com - PUT DOCS PR HERE

Full changelog

[Implement ...]

Issue reference

Fix #[issue number]

Hey @bungle could you please explain further ?
I am not sure where to make the change

Hey @bungle could you please explain further ?
I am not sure where to make the change

The Kong Testing Intermidiate CA comes from:
https://github.com/Kong/kong/blob/master/t/certs/intermediate.crt
To fix that you need to generate new intermediate.crt that does not contain this typo.

Closing because stale

authors: bungle hbagdi jbampton


text:
chore(patches): cleanup the pcre2 regex patch

Summary
This adds two commits from @zhongweiy as found here: https://github.com/openresty/lua-nginx-module/pull/2291 https://github.com/openresty/stream-lua-nginx-module/pull/341
They are cleanups to original patch.
KAG-3614

Thanks! The 2 mentioned PRs have been merged in upstream.

authors: bungle zhongweiy


text:
hotfix(healthchecks) make circuit-breaker functionality off by default

To avoid unexpected behaviors for users, this makes the circuit-breaker functionality of passive healthchecks disabled by default. Users need to opt-in by supplying threshold values to enable the cut-off of targets based on analysis of ongoing traffic.
This commit requires functionality introduced in lua-resty-healthcheck 0.3.0 (version bump included in this PR).

@thibaultcha I updated all counters to 0, as per your comment.


text:
How to add NGINX 3rd Party Modules in kong?

Ingress or openresty can add a module this way:
./configure --add-module=path/to/nginx_upload_module

But configure is not found in the kubernetes-ingress-controller

You will need to use https://github.com/kong/openresty-build-tools to build a valid OpenResty that is usable by Kong and then install that.

authors: HefeiJoe hbagdi

repo: Kong/kubernetes-ingress-controller


text:
feat(plugins) support plugin ordering

What this PR does / why we need it:
Yet more PRs because GitHub Actions got sad and didn't want to run on the old one anymore.
Adds support for Plugin Ordering.
Adds integration tests against nightly Kong images. Currently omits nightly tests of OSS images because they're not ready yet. I don't have any tracking information from the enablement team about when those tags will be available, unfortunately.
Which issue this PR fixes (optional, in fixes #<issue number>(, fixes #<issue_number>, ...) format, will close that issue when PR gets merged): Fix https://github.com/Kong/kubernetes-ingress-controller/issues/2656
Special notes for your reviewer:
PR Readiness Checklist:
Complete these before marking the PR as ready to review:

[x] the CHANGELOG.md release notes have been updated to reflect any significant (and particularly user-facing) changes introduced by this PR
](https://github.com/Kong/kubernetes-ingress-controller/pull/2657)%5D(https://github.com/Kong/kubernetes-ingress-controller/pull/2657)

GitHub's way of telling you (or rather, not telling you, since it just says "startup failure" and nothing else) that "an action can't run because it needs a step that doesn't exist" is very bad :(

I guess you'll need to make linter happy though

authors: pmalek rainest


text:
feat: handle KongServiceFacade Ingress backends

What this PR does / why we need it:
Implements KongServiceFacade as a possible backend of Ingress rules along with unit tests. It extends the translator and subtranslator packages responsible for translation Ingress into Kong Services and Routes so they take into account that KongServiceFacade can be an Ingress backend (defined in Rule.Backend.Resource field).
Which issue this PR fixes:
Part of #5152.
Notes to reviewers:
I tried my best to keep this portion of changes to be as small as possible - most of the PR volume is unit tests and golden tests. The rest is translator/subtranslator package changes that generally are about pushing KongServiceFacade as a new type of Kong Service backend which I couldn't split.
There will be one more PR adding an integration test verifying the new functionality.
<!--
**PR Readiness Checklist**:

Complete these before marking the PR as `ready to review`:

- [ ] the `CHANGELOG.md` release notes have been updated to reflect any significant (and particularly user-facing) changes introduced by this PR

In telemetry_test.go change feature-servicefacade -> feature-kongservicefacade, to fix failing envtest


text:
Support short domains for Kntive cluster local services

What this PR does / why we need it:
This patch supports short domains for Kntive cluster local services.
After this patch, users can access hello.default or
hello.default.svc for hello.default.svc.cluster.local
Fixes https://github.com/Kong/kubernetes-ingress-controller/issues/803

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.

Please rebase on top of the next branch.

Sure, done.

Question: Why does knative not populate the Knative Ingress resource with the necessary hosts? It should do so if Knative wants it's services to be accessible on the short domains.

@nak3 Can we close this since we are taking care of the feature in https://github.com/knative/serving/pull/9100?

authors: CLAassistant hbagdi nak3


text:
Copy Account address although don't click copy address button when user click on Manage Account icon

Description the bug
Copy Address Account when click on Manage Account
To reproduce:
Steps:

Open the extension
Go to Import Account
Copy the SeedPhase & Paste to "Mnemonic Seed" field
Input valid all data & created account successfully
Click on Manage Account icon
Then paste value on other area & check this value

Actual: Automatically copy account address although don't click copy address button
Expect: When user click on Manage Account icon, do not copy account address

This bug is caused by clicking the settings menu, this action also clicks on the Identicon (Account avatar) and copies its address.

The solution is to create a mask on the Identicon of the settings menu button.

Validation passed!

repo: Koniverse/SubWallet-Extension


text:
[Issue-1872] Allow paste Amount to send

Related issue(s)

#1872

Is your feature request related to a problem(s)? Please describe.

Allow to paste amount

Describe the solution you'd like

Allow to paste amount

Describe alternatives you've considered

No

Additional context

No

🚀 Deployed on https://koni-dev-issue-1872--sw-web-runner.netlify.app


text:
Add Chinese language?

I briefly translated the Chinese language myself. if it is possible. You can add it. This mod is very good.
(However, I cannot Chineseize the creature name and status. I hope you can improve it in the future.)

It is a different mod, not related to playerAnimator.

authors: KosmX QianMuu

repo: KosmX/minecraftPlayerAnimator


text:
Breaking change after 1.7.3 that prevents creation of directory structure

Describe the bug
In 1.7.3, if "Self-contained Documents" was selected as the export preset and a directory with files in it was selected, the HTML output would reflect the directory structure. This is no longer the case with master.
I've checked out master and have tried selecting "HTML Documents" as the export preset to generate self contained documents and only the files are created, no directories.
Operating System
macOS Catalina 10.15.17
To Reproduce
Steps to reproduce the behavior:
The following is how to see the expected behavior.

Checkout tag 1.7.3.
Select a directory in the file picker popup so that it selects the directory and the files within.
Select "Self-contained Documents" for the export preset.
Select export directory.
Export
Check export files. File structure should reflect what was selected in the file picker pop up.

The following shows the unexpected behavior.

Checkout master
Select a directory in the file picker popup so that it selects the directory and the files within.
Select "HTML Documents" for the export preset.
Select export directory.
Export
Check export files. File structure will be flat with no directories.

Hi, first of all master is not guarenteed to have the latest release in it, rather it tends to have the latest working code. I am not the most organized here.
Some people want the file structure flat while some want the folder structure. Maybe I just need to add a toggle for it,

Thanks for the quick response! As far as the file structure being flat or having directories, I've found that the HTML file tree navigation on the left side bar doesn't work when the structure is flat since it's not able to properly locate the files.
I've submitted a PR to include directories in the file structure, though it doesn't include a toggle. At any rate, it fixes the file tree navigation issues.

Thanks I will look at the PR

repo: KosmosisDire/obsidian-webpage-export


text:
Lazy calculations

Give the user the ability to control the timing of calculations (stats, etc.)

@AndreiKingsley
Could you provide more details about this issue?

No we evaluate all data transformation inside the DSL, but sometimes we want to postpone these calculations.

I think we should discuss about this

authors: AndreiKingsley devcrocod

repo: Kotlin/kandy


text:
Unable to run XML generator after upgrading to 0.7.2 (or later) from 0.7.1.

Describe the bug
We are unable to generate XML reports (or HTML reports for that matter) after upgrading to 0.7.2 from 0.7.1. Are there any recommended "migration" steps needed to be done after this upgrade, or any known issues why a configuration that works with 0.7.1 may not work with 0.7.2?
Note that note all of our subprojects produce the error above, but it is not obvious as to why we do see this error in some cases and only after upgrading from 0.7.1 to 0.7.2.
Errors
663: Task failed with an exception.
-----------
* What went wrong:
Some problems were found with the configuration of task ':libraries:path:to:subproject:koverXmlReport' (type 'KoverXmlTask').
  - In plugin 'org.jetbrains.kotlinx.kover' type 'kotlinx.kover.gradle.plugin.tasks.reports.KoverXmlTask' property 'filters' doesn't have a configured value.
    Reason: This property isn't marked as optional and no value has been configured.
    Possible solutions:
      1. Assign a value to 'filters'.
      2. Mark property 'filters' as optional.
    For more information, please refer to https://docs.gradle.org/8.4/userguide/validation_problems.html#value_not_set
 in the Gradle documentation.
  - In plugin 'org.jetbrains.kotlinx.kover' type 'kotlinx.kover.gradle.plugin.tasks.reports.KoverXmlTask' property 'reportFile$kover_gradle_plugin' doesn't have a configured value.
    Reason: This property isn't marked as optional and no value has been configured.
    Possible solutions:
      1. Assign a value to 'reportFile$kover_gradle_plugin'.
      2. Mark property 'reportFile$kover_gradle_plugin' as optional.
    For more information, please refer to https://docs.gradle.org/8.4/userguide/validation_problems.html#value_not_set
 in the Gradle documentation.

Reproducer
We don't have a sample project to share, our main project is close source but here is a description of our setup:
Our project has a large number of subproject, and it is setup with the following configuration in our top-level build.gradle.kts, notice that the configuration does have a "filters" section and a call to "setReportFile()". This configuration propagates down to all of our subprojects.
  // Configuring the kover (code coverage) plugin
  pluginManager.withPlugin("org.jetbrains.kotlinx.kover") {
    val codeCoverageIncludes: List<String> = listOf("<some files>")
    val codeCoverageExcludes: List<String> = listOf("<some files>")

    // Common report configuration
    val config =
      Action<KoverReportsConfig> {
        // configure html report Kover task
        html {
          // change report directory
          setReportDir(layout.buildDirectory.dir("code-coverage/html-report"))
        }

        // configure xml report Kover task
        xml {
          // change report directory & report file name
          setReportFile(
            layout.buildDirectory.file("code-coverage/xml-report/code-coverage-result.xml")
          )
        }
      }

    extensions.configure(KoverReportExtension::class) {
      // Common filters
      filters {
        excludes {
          classes(codeCoverageExcludes)
          packages("<some packages>")
          annotatedBy("<some string>")
        }
        includes { classes(codeCoverageIncludes) }
      }

      // Use the same variant as ciUnitTest if it's an android project
      val androidTestVariant =
        if (plugins.hasPlugin("com.android.base")) {
          SlackProperties(project).ciUnitTestVariant
        } else {
          null
        }

      // Default reports (JVM, Multiplatform)
      defaults {
        androidTestVariant?.let(::mergeWith)
        config.execute(this)
      }

      androidTestVariant?.let {
        // Android reports
        androidReports(variant = it, config = config)
      }
    }
  }

Environment

Kover Gradle Plugin version: 0.7.2
Gradle version: AGP 8.1.2
Kotlin project type: Kotlin/Android

Hi,
could you please clarify a few questions:

have you tried version 0.7.3 or 0.7.4

pluginManager.withPlugin("org.jetbrains.kotlinx.kover") {
is this script used only in the root project?
If yes, then it is enough to write after using the Kover plugin in the plugins block
koverReport {
    // Common filters
    filters {
      // ...
    }

      // Default reports (JVM, Multiplatform)
    defaults {
        // ...
      }
}

how do you apply Kover plugin in project (and subprojects)?
are build variants created dynamically in your projects?
Which command do you run Kover tasks with?   androidReports(variant = it, config = config)
Using androidReports(variant = it, config = config) looks superfluous, because the same settings are applied to reports by default and koverXmlReport can be runned always

Also, for flexibility, it is always better to use a lazy Gradle DSL.
Instead of
if (plugins.hasPlugin("com.android.base")) {

it is better to use
pluginManager.withPlugin("com.android.base") {
    koverReport {
        defaults {
            mergeWith(SlackProperties(project).ciUnitTestVariant)

            // config file names
        }

        // no `androidReports` config if always executes only `koverXmlReport` command
    }
}

Hello, thanks for looking into the problem. We'll try out some of the changes you mention, it is possible we are configuring something incorrectly that only became apparent with 0.7.2.
In the meantime, I'll attempt to answer your questions:

Yes. We first jump straight up to 0.7.4, that's where we first saw the issue. We then went back down the different releases to see where the issues started and kept seeing the same issues with anything higher than 0.7.1.

our gradle file has a "subprojects" section where we list all the plugins/configurations that are propagated down to the subproject:

subprojects {
  pluginManager.withPlugin("<plugin>") {
  }
  pluginManager.withPlugin("org.jetbrains.kotlinx.kover") {
  }
...
}

I am not sure I understand what you need to know here 😅

yes! We have a few variants that we generate for different usages.

That's probably because of the different variants we have, since our tests only run on one (or two) of the variants. The command we use is: ./gradlew koverXmlReport , we pass a couple of extra parameters for gradle here such as --no-configuration-cache and --exclude-task <task>, so some subprojects may not need to generate a report.

Hello,
It seems like I have a similar — if not the same — issue.
Reproducer: https://github.com/armatys/KoverTest/tree/kover_074_gradle_81 (note: it's on a branch called kover_074_gradle_81)
Basically, I'm applying the kover plugin in afterEvaluate. Then, run any of the commands: ./gradlew koverVerify, ./gradlew koverHtmlReport or ./gradlew koverXmlReport and it will fail.
In 0.7.1. it worked fine, but it fails in 0.7.2, 0.7.3 and 0.7.4.

I'm applying the kover plugin in after evaluate, because I'm using a custom convention plugin with a custom extension object, so I need to wait until afterEvaluate to get the proper configuration values.
I could move the application of kover plugin outside of afterEvaluate block, but then, the lib project is not configured properly (see classes(ext.excludeClasses.get()) in CustomPlugin). The class "com.example.lib.MyClass" is not excluded from the report if the plugin is applied immediately.
I guess it could be solved with #410 ?

@armatys

I guess it could be solved with https://github.com/Kotlin/kotlinx-kover/issues/410 ?

Yes, the implementing of #410 should simplify the use of convention plugins and it should solve your problem.

Closed as dublicate of #410.
Feel free to open new issue if the problem persists after the release of #410.

authors: armatys jhperezd shanshin

repo: Kotlin/kotlinx-kover


text:
🛑 🍚🛍 TextTextBaby WWW DE is down

In fc54603, 🍚🛍 TextTextBaby WWW DE (https://www.texttextbaby.de) was down:

HTTP code: 0
Response time: 0 ms

Resolved: 🍚🛍 TextTextBaby WWW DE is back up in 3d80133 after 33 minutes.

repo: KrisKodira/uptime-monitor


text:
Komponenta pretplate

Treba izraditi komponentu pretplate koja će se pojavljivati na
http://localhost:8080/Subscription
http://localhost:8080/options

za sada neka bude komponenta koja sadrži neki naslov, cijenu, malo teksta i gumb za signup...doradimo to dalje kad otkrijemo kako diše Stripe ili koji ćemo već payment gateway koristiti

Spremno za testiranje

Spremno za testiranje

@Marko-Tomljanovic povezuje isto s modelima pretplate sljedeći rješenje s https://payingcustomer.netlify.app/test

@Marko-Tomljanovic povezuje isto s modelima pretplate sljedeći rješenje s https://payingcustomer.netlify.app/test

@IvanaPolonijo uspješno napravljeno, spremno za testiranje.
https://user-images.githubusercontent.com/56721240/105194685-21e7c400-5b3a-11eb-9546-34365f0bd7c5.mp4

@IvanaPolonijo uspješno napravljeno, spremno za testiranje.
https://user-images.githubusercontent.com/56721240/105194685-21e7c400-5b3a-11eb-9546-34365f0bd7c5.mp4

authors: IvanaPolonijo Marko-Tomljanovic

repo: KristijanCetina/payingCustomer


text:
added login, register and forget password Page

i haven't assigned this kind of issue to anyone
also i don't want to add this


text:
add new end2end demo

This is the new end2end demo script
python demo.py --video_path siren.mp4 --output_video_path outputs/siren/img1 --weight DanceTrack_yolox_s.tar --yolox_type yolox-s \
        --output_txt_path outputs --config ./configs/demo_test.yaml --dataset DanceTrack

덕분에 정말 빠르게 Demo 돌려보았습니다. 감사드립니다. 🥲

repo: Kroery/DiffMOT


text:
merge package from arlac77/npm-package-template

package.json

chore(package): semantic-release@^15.7.0

:tada: This PR is included in version 3.0.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


text:
merge package template from Kronos-Tools/npm-package-template

Updated standard to latest version

Codecov Report

Merging #216 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master     #216   +/-   ##
=======================================
  Coverage   88.42%   88.42%
=======================================
  Files           1        1
  Lines         216      216
=======================================
  Hits          191      191
  Misses         25       25

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update b75914a...391ef8a. Read the comment docs.

repo: Kronos-Integration/kronos-service


text:
merge from arlac77/npm-package-template

package.json

chore(package): semantic-release@^15.13.28
chore(scripts): cover@#overwrite c8 -x 'tests/**/*.mjs' --temp-directory build/tmp ava && c8 report -r lcov -o build/coverage --temp-directory build/tmp
chore(package): set $.ava.require='esm' as in template
chore(package): set $.ava.files='tests/*-test.mjs' as in template
chore(package): set $.ava.extensions='mjs' as in template

:tada: This PR is included in version 2.0.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:

repo: Kronos-Integration/service-uti


text:
Benchmark samples

Known issues
Suave.LibUv on Mono still suffers from https://github.com/SuaveIO/suave/issues/360 and cannot get an accurate benchmark.
The Suave.LibUv on CoreCLR has trouble resolving packages and cannot be properly benchmarked
    Package Suave.LibUv 2.0.0-alpha4 is not compatible with netcoreapp1.0 (.NETCoreApp,Version=v1.0). Package Suave.LibUv 2.0.0-alpha4 supports: net40 (.NETFramework,Version=v4.0)
    Package FSharp.Core 3.1.2.5 is not compatible with netcoreapp1.0 (.NETCoreApp,Version=v1.0). Package FSharp.Core 3.1.2.5 supports:
      - net20 (.NETFramework,Version=v2.0)
      - net40 (.NETFramework,Version=v4.0)
      - portable-monoandroid10+monotouch10+net45+xamarinios10 (.NETPortable,Version=v0.0,Profile=net45+monoandroid10+monotouch10+xamarinios10)
      - portable-net45+sl5+win8 (.NETPortable,Version=v0.0,Profile=Profile47)
      - portable-net45+win8 (.NETPortable,Version=v0.0,Profile=Profile7)
      - portable-net45+win8+wp8 (.NETPortable,Version=v0.0,Profile=Profile78)
      - portable-net45+win8+wp8+wpa81 (.NETPortable,Version=v0.0,Profile=Profile259)
    One or more packages are incompatible with .NETCoreApp,Version=v1.0.

Also
What should I do about the giant  samples/SuaveOnMono/paket.lock files?
Anything else I should add?

Whoops looks like I need to rebase.  I'll get on that.

Let's put samples used for benchmark to benchmark folder, not to samples
I think that pinning framework version in paket.dependencies for mono versions should make both fsproj and paket.lock smaller

Yep, that helped considerably.

Thanks!

authors: Krzysztof-Cieslak TheAngryByrd


text:
Custom DownloadUrl

I'm submitting a...

[ ] Regression (a behavior that used to work and stopped working in a new release)
[ ] Bug report
[x] Feature request
[ ] Documentation issue or request
[ ] Support request

As per https://github.com/Ks89/angular-modal-gallery/issues/183#issuecomment-669228041
Current behavior

Calling modalGalleryService.open with the option config.libConfig.currentImageConfig.downloadable: true results in a download button on the page which downloads the file specified in modal.img
Expected behavior

It would be great that the download button downloads the "extUrl" or another property than "img".
Maybe a "downloadUrl" property which takes priority over img when specified.
What is the motivation / use case for changing the behavior?

The reason behind this is that we show a resized image on the Webapp and allow the download of the full sized image.
Environment (the most important section to fill very carefully)

- @ks89/angular-modal-gallery version: 8.0.0-beta.4
- Node version: 12.14.1
- npm version: 6.14.5
- Operating System and version: macOS 10.15.6
- Angular version: 9.1.11
- angular-cli version (or SystemJS/Webpack): 9.1.9
- I'm using Server Side Rendering with angular-universal: NO
- I'm compiling with mode: PROD with AOT

Browser:
- [ ] Chrome (desktop) version 85.0.4183.83

Others:

good idea to create a dedicated issue


text:
fixing some deps

Fixing some ambiguously versioned deps to align versions of webpack

@openshift-ci-robot /help

@openshift-merge-robot /help

repo: Kuadrant/console-plugin


text:
[ Feature ] [ Wakala Fee ] Should be sent to the kukuza treasury.

[x] @steffenkrogmann Provide treasury wallet address.
[ ] @Allen-Muhani  build and test the concept.

Kukuza Treasury on Alfajores Testnet:
0xfF096016A3B65cdDa688a8f7237Ac94f3EFBa245

@steffenkrogmann
Here are the pull requests for the changes and the test cases.
https://github.com/Kukuza/kukuza-contracts-monorepo/pull/30
https://github.com/Kukuza/kukuza-contracts-monorepo/pull/31

repo: Kukuza/kukuza-contracts-monorepo


text:
Interpolation Search in C, C++, Java & Python

Is your feature request related to a problem? Please describe.
Given a sorted array of n uniformly distributed values arr[], write a function to search for a particular element x in the array.
Linear Search finds the element in O(n) time, Jump Search takes O(√n) time and Binary Search  takes O(log n) time.
The Interpolation Search is an improvement over Binary Search for instances, where the values in a sorted array are uniformly distributed. Interpolation constructs new data points within the range of a discrete set of known data points. Binary Search always goes to the middle element to check. On the other hand, interpolation search may go to different locations according to the value of the key being searched. For example, if the value of the key is closer to the last element, interpolation search is likely to start search toward the end side.
Describe the solution you'd like
Step1: In a loop, calculate the value of “pos” using the probe position formula.
Step2: If it is a match, return the index of the item, and exit.
Step3: If the item is less than arr[pos], calculate the probe position of the left sub-array. Otherwise, calculate the same in the right sub-array.
Step4: Repeat until a match is found or the sub-array reduces to zero.

Additional context

@Kumar-laxmi I can do in all 4 languages assign me

@Kumar-laxmi  i want to work on this issue
i can do in all language or  c++

Please assign me this issue it will be my first pr i can do it in C++ C python java

@Kumar-laxmi  I can do it in all languages mentioned. Please assign this to me

Assigned!
@mandliyarajendra11 : C, C++, Java & Python

repo: Kumar-laxmi/Algorithms


text:
Z Algorithm

Not a bug or problem.Actually I want to include Z Algorithm which is a pattern searching Algorithm.It is used to search for occurrences of given pattern in string.
Example-:
String-a b c d e a a b b t  a a b d  f g
Pattern-a a b b
New string-a a b b # a b c d e a a b b t  a a b d  f g
Z-array-a a b b # a b c d e a a b b t  a a b d  f g
0 1 0 0 0 1 0 0 0 0 4 1 0 0 0 3 1 0 0 0 0
when we generate the Z array, we are basically searching for the first 4 characters of our string, which is also our pattern. As we have added an additional character, the largest pattern that we can find will be the pattern we need to search for.
The first value of the Z array is explicitly set to zero. As we can see in the array at index 1, as 'a' matches with the character at index 0('a'), the value is set to 1. Then as none of the values are matching, they are set to zero. Now let's see the value at index 10. It is set to 4, as 4 characters starting from this index, match the prefix of the string(starting 4 characters).
This speeds up the algorithm to a great extent as many elements are skipped. If this technique is not used, the algorithm would perform computations for all the elements, and thus get reduced to a quadratic [O(n^2n )] algorithm, equivalent to naive pattern searching.
Please assign me this issue:C and C++

@Ayatisonkar Could you implement this in all four languages

Yes sir I can @Kumar-laxmi

Assigned!
@Ayatisonkar : C, C++, Java, Python

Sir can you please assign me  C# language instead of C @Kumar-laxmi

@Ayatisonkar In this repo we are only working with C, C++, Java, Python

I am pretty sure that you can implement in C if you can in C#

So sir please assign me only java python and c++

authors: Ayatisonkar Kumar-laxmi


text:
Fractional knapsack problem

Description
This PR adds the implementation of the fractional knapsack algorithm to the Algorithms repository. The fractional knapsack algorithm is used to solve the fractional knapsack problem, which involves selecting items with maximum total value while respecting a weight constraint.
Changes Made

Added a new file Fractional Knapsack folder  to the 'Dynamic Programming folder of Algorithms directory for C++, Java and Python language, containing the implementation of the fractional knapsack algorithm.
Added comments to explain each step of the algorithm and provide insights into the problem it solves.
Added README file for better understanding.

Testing

Ran the algorithm on various input scenarios and compared the results with expected outcomes.
All test cases passed successfully, demonstrating the correctness of the implementation.

Related Issue
issue no. #688
Checklist

[x] The code follows the coding style guidelines of the repository.
[x] The code is well-documented with comments to improve readability.
[x] Tests have been added to ensure the correctness of the algorithm.
[x] The code has been tested locally and runs without any errors.
[x] All existing tests pass successfully.
[x] The PR has a descriptive title and provides sufficient information.

Please review this PR and let me know if any further modifications or changes are required. Thank you!

Kindly sort CodeFactor issues

@manu31shukla Kindly include the Screen-shot of your implementation


text:
[Enhancement] kpm init: Initialize the kcl project.

kpm init : Initialize the kcl project.

kpm init
The usage of kpm init.
$ kpm init
kpm: module name must be specified.
kpm: run 'kpm init help' for more information.

$ kpm init help
NAME:
   kpm init - initialize new module in current directory

USAGE:
   kpm init [command options] [arguments...]

OPTIONS:
   --help, -h  show help

$ kpm init mykcl
kpm: creating new kcl.mod: /Users/kpm/kcl.mod
kpm: package ' mykcl ' init finished

Execute this command inside a kcl package.
$ kpm init mykcl
kpm: '/Users/kpm/kcl.mod' already exists

The default kcl.mod file are as follows
[package]
  name = "mykcl"
  version = "0.0.1"
  edition = "0.0.1"

repo: KusionStack/kpm


text:
Dedicated server support

How this master server works - it responsible for games list, inventories, rooms list and games' starting. But it is not responsible for games' hosting! Games' hosting should be the main priority of master server. There is more detailed explanation:
You host master server on dedicated server with an external IP. Then, when player without external IP creates a room on that master server, other players can enter his lobby successfully because this is what master server do. But when the game is started, it is being hosted on this host-player 192.168.0.1 / 127.0.0.1 / localhost IP. So all players who were waited for game start are kicked with connection error (error 2004).
All games on master server should be hosted with the same IP as master server. It should just increment a port number for each room and not use players' IP's for hosting. So room's IP's will look like xxxxx:30001, xxxxx:30002, xxxxx:30003, etc. This is how it is done in CSO1 and maybe was done in CSO2. This will give an ability to play online while having only the master server IP.

+1

+1

I have external IP ，But so do others.2004，2001，7004

Stuck in% 15 or 10%

You can use OpenVPN
create a server
all players connect it
DCHP server must open

still no improvement?

still no improvement?

i haven't looked into this yet

authors: ChenyuHou Kawaii-Maou-Sama L-Leite Riseless gnexia2013 organnerx


text:
thorvald: 1.2.2-1 in 'kinetic/lcas-dist.yaml' [bloom]

Increasing version of package(s) in repository thorvald to 1.2.2-1:

upstream repository: https://github.com/LCAS/Thorvald.git
release repository: https://github.com/LCAS/thorvald-releases.git
distro file: kinetic/lcas-dist.yaml
bloom version: 0.9.0
previous version for package: 1.2.1-1

thorvald
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* Contributors: Marc Hanheide

thorvald_2dnav
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* Contributors: Marc Hanheide

thorvald_base
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* Contributors: Marc Hanheide

thorvald_bringup
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* Contributors: Marc Hanheide

thorvald_can_devices
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* Contributors: Marc Hanheide

thorvald_description
* remove uncessary kinect2 dep
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* removed velodyne dep
* Contributors: Marc Hanheide

thorvald_example_robots
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* Contributors: Marc Hanheide

thorvald_gazebo_plugins
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* Contributors: Marc Hanheide

thorvald_gui
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* Contributors: Marc Hanheide

thorvald_msgs
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* Contributors: Marc Hanheide

thorvald_simulator
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* Contributors: Marc Hanheide

thorvald_teleop
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* Contributors: Marc Hanheide

thorvald_twist_mux
* Merge branch 'kinetic-devel' of https://github.com/LCAS/Thorvald into kinetic-devel
* Contributors: Marc Hanheide

irrelevant

repo: LCAS/rosdistro


text:
Make Help menu scrollable

I added additional links to the Help & Resources pop-out menu. Now the bottom part of the list of resources is not visible, and the pop-out sidebar does not scroll. Can we make this scroll? Otherwise we need to change the design some other way so that all the text is visible, without making the font too small.

I changed the spacing between sections of links to fit everything on the main screen.

fixed by #437


text:
🛑 [Web] Blog is down

In c652ccd, [Web] Blog (https://rongyi.blog) was down:

HTTP code: 0
Response time: 0 ms

Resolved: [Web] Blog is back up in e68150c.


text:
无法进入断点

KCObjcBuild[80175:963663] Hello, World! KCObjcBuild was compiled with optimization - stepping may behave oddly; variables may not be available.
目前无法正确的使用断点，感觉整个是乱的，查过相关配置，是debug模式，并且优化等级为None
Mac 系统 12.0.1 Xcode版本13.2.1

KC 重磅提示 调试工程很重要 源码直观就是爽
⚠编译调试不能过: 请你检查以下几小点⚠
①: 编译 targets 选择: KCObjcBuild
②: enable hardened runtime -> NO
③: build phase -> denpendenice -> objc
④: team 选择 None
iOS进阶内容重磅分享 微信认准: KC_Cooci 麻烦来一个 👍

@littlechaochao  解决了吗？我也是进不去断点

如果你们调试还是进不去 直接微信我: KCCooci

authors: LGCooci littlechaochao songhongkang


text:
Added new test : Netperf for checking PPS performance

This commit adds following variants of the test :

PERF-NETWORK-TCP-NETPERF-SINGLE-PPS-Synthetic
PERF-NETWORK-TCP-NETPERF-SINGLE-PPS-SRIOV
PERF-NETWORK-TCP-NETPERF-MAX-PPS-Synthetic
PERF-NETWORK-TCP-NETPERF-MAX-PPS-SRIOV

Note: Test execution is being verified for all the tests. Will post the results here once done.

Execution is verified.
09/20/2018 12:12:23 : [INFO ] ~~~~~~~~~~~~~~~TEST END : PERF-NETWORK-TCP-NETPERF-MAX-PPS-SRIOV~~~~~~~~~~
09/20/2018 12:12:23 : [INFO ] CURRENT - PASS    - 2
09/20/2018 12:12:23 : [INFO ] CURRENT - FAIL    - 0
09/20/2018 12:12:23 : [INFO ] CURRENT - ABORTED - 0
09/20/2018 12:12:23 : [INFO ] Checking background cleanup jobs..
09/20/2018 12:12:23 : [INFO ] Removed 0 context files.
09/20/2018 12:12:23 : [INFO ] Cycle Finished.. TC-16139
09/20/2018 12:12:23 : [INFO ]
Test Results Summary
ICA test run on 09/20/2018 11:27:28
ARM Image under test Canonical : UbuntuServer : 16.04-LTS : latest
Total Executed TestCases        2 (       2 Pass,        0 Fail,        0 Abort)
Total Execution Time(dd:hh:mm) 0:0:22
XML file: TestConfiguration

PERF-NETWORK-TCP-NETPERF-SINGLE-PPS-SRIOV Execution Time: 13.5 minutes
        PERF-NETWORK-TCP-NETPERF-SINGLE-PPS-SRIOV : PASS
                        Rx Average PPS : 17536
                        Rx Minimum PPS : 12245
                        Rx Maximum PPS : 19107
PERF-NETWORK-TCP-NETPERF-MAX-PPS-SRIOV Execution Time: 9.89 minutes
        PERF-NETWORK-TCP-NETPERF-MAX-PPS-SRIOV : PASS
                        Rx Average PPS : 263729
                        Rx Minimum PPS : 252107
                        Rx Maximum PPS : 270721

Logs can be found at .\TestResults\2018-20-09-16-57-14-3647

09/20/2018 12:12:23 : [INFO ] Creating 'Azure-16139-buildlogs.zip' from '.\TestResults\2018-20-09-16-57-14-3647'
09/20/2018 12:12:23 : [INFO ] D:\GitHub\LISAv2\Azure-16139-buildlogs.zip created successfully.
09/20/2018 12:12:24 : [INFO ] Copied : .\report\report_TC-16139.xml --> .\report\report_TC-16139-junit.xml
09/20/2018 12:12:24 : [INFO ] Analysing results..
09/20/2018 12:12:24 : [INFO ] PASS  : 2
09/20/2018 12:12:24 : [INFO ] FAIL  : 0
09/20/2018 12:12:24 : [INFO ] ABORT : 0
09/20/2018 12:12:24 : [INFO ] LISAv2 exits with code: 0


text:
RZNevada Pip Install of python2 fails

python2 -m virtualenv --system-site-packages --python=python2.7
/usr/bin/python2: No module named virtualenv

python3 -m virtualenv --system-site-packages --python=python2.7 /usr/gapps/ats/toss_4_x86_64_ib_cray/7.0.6
/usr/bin/python3: No module named virtualenv
May have to work with LC or Gyllenhall or someone on getting this supported on RZNevada

email sent to LC/Gyllenhaal about this

repo: LLNL/ATS


text:
Bugfix/gunney/inconsistent allocator distributed closest point

Fix unneeded copies across memory spaces in DistributedClosestPoint
Inconsistent allocator usage placed data in different memory spaces.  This requires copies that cause performance issues in DistributedClosestPoint.  We now use the same allocator on all the data used in the search operation.

This PR is a bugfix
It does the following:

Sets the custom allocator earlier in the set-up to ensure the allocator is used throughout the search.
Fixes a couple of places in DistributedClosestPoint where the default allocator was used instead of the custom allocator.

Thanks for the prompt reviews @agcapps and @kennyweiss

repo: LLNL/axom


text:
Revert "add extrap import in init.py (#79)"

This reverts commit 5b4f8bac928f78f5fdea01dc7ee755cd6f5dd0d7. Extra-p is not a required dependency.

@slabasan So what is the recommended way to import Extra-P without this?

import thicket as th
from thicket.model_extrap import Modeling

mdl = Modeling(
    th_ens,
    "rank",
    chosen_metrics=[
        "Total time_mean",
    ],
)

repo: LLNL/thicket