Related
I have stored in iCloud several JSON files as a Byte type. Hope that's correct so far.
I've got to fetch those CKRecords and then parse them and show a graph using the values stored in the JSON. I am capable of fetching the data from iCloud but I got no luck parsing this JSON.
{"00:00": 17, "00:10": 16, "00:20": 17, "00:30": 16, "00:40": 16, "00:50": 17, "01:00": 16, "01:10": 16, "01:20": 16, "01:30": 16, "01:40": 17, "01:50": 17, "02:00": 18, "02:10": 18, "02:20": 17, "02:30": 17, "02:40": 17, "02:50": 17, "03:00": 16, "03:10": 17, "03:20": 16, "03:30": 16, "03:40": 17, "03:50": 16, "04:00": 16, "04:10": 16, "04:20": 16, "04:30": 16, "04:40": 16, "04:50": 18, "05:00": 17, "05:10": 16, "05:20": 17, "05:30": 17, "05:40": 17, "05:50": 17, "06:00": 17, "06:10": 17, "06:20": 16, "06:30": 16, "06:40": 17, "06:50": 15, "07:00": 15, "07:10": 15, "07:20": 14, "07:30": 15, "07:40": 13, "07:50": 11, "08:00": 8, "08:10": 8, "08:20": 7, "08:30": 5, "08:40": 4, "08:50": 4, "09:00": 2, "09:10": 2, "09:20": 9, "09:30": 8, "09:40": 7, "09:50": 7, "10:00": 5, "10:10": 4, "10:20": 6, "10:30": 5, "10:40": 4, "10:50": 4, "11:00": 3, "11:10": 2, "11:20": 2, "11:30": 1, "11:40": 2, "11:50": 1, "12:00": 1, "12:10": 2, "12:20": 3, "12:30": 3, "12:40": 2, "12:50": 3, "13:00": 1, "13:10": 0, "13:20": 1, "13:30": 0, "13:40": 3, "13:50": 2, "14:00": 3, "14:10": 4, "14:20": 3, "14:30": 6, "14:40": 4, "14:50": 6, "15:00": 5, "15:10": 7, "15:20": 7, "15:30": 7, "15:40": 7, "15:50": 6, "16:00": 7, "16:10": 8, "16:20": 8, "16:30": 6, "16:40": 5, "16:50": 5, "17:00": 5, "17:10": 4, "17:20": 4, "17:30": 6, "17:40": 6, "17:50": 5, "18:00": 6, "18:10": 7, "18:20": 4, "18:30": 3, "18:40": 3, "18:50": 5, "19:00": 5, "19:10": 3, "19:20": 4, "19:30": 4, "19:40": 2, "19:50": 4, "20:00": 1, "20:10": 2, "20:20": 2, "20:30": 1, "20:40": 3, "20:50": 2, "21:00": 4, "21:10": 4, "21:20": 7, "21:30": 7, "21:40": 6, "21:50": 7, "22:00": 8, "22:10": 7, "22:20": 8, "22:30": 9, "22:40": 10, "22:50": 10, "23:00": 10, "23:10": 9, "23:20": 9, "23:30": 10, "23:40": 10, "23:50": 10}
I previously used Codable to parse a JSON using Swift but this case is different. I got a lot of keys that are numbers and I don't really know how to approach this.
you can try
let res = try? JSONDecoder().decode([String:Int].self,from:data)
I would like to know how can I split my table into subtables of 9's.
Example:
{ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 }
Code shall return:
{ {1, 2, 3, 4, 5, 6, 7, 8, 9}, {10, 11, 12, 13, 14, 15, 16, 17, 18}, { 19, 20} }
How do you think is this done?
Your code seems over complicated. The task is to create a subtable every 9 elements. The code below does that:
a={ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 }
b={}
j=0
k=9
for i=1,#a do
if k==9 then j=j+1; b[j]={}; k=0 end
k=k+1
b[j][k]=a[i]
end
Here, j tracks the number of subtables created and k tracks the number of elements added to a subtable. When k becomes 9, a new subtable is created. k starts at 9 to signal that.
I have text data as follows.
X_train_orignal= np.array(['OC(=O)C1=C(Cl)C=CC=C1Cl', 'OC(=O)C1=C(Cl)C=C(Cl)C=C1Cl',
'OC(=O)C1=CC=CC(=C1Cl)Cl', 'OC(=O)C1=CC(=CC=C1Cl)Cl',
'OC1=C(C=C(C=C1)[N+]([O-])=O)[N+]([O-])=O'])
As it is evident that different sequences have different length. How can I zero pad the sequence on both sides of the sequence to some maximum length. And then convert each sequence into one hot encoding based on each characters?
Try:
I used the following keras API but it doesn't work with strings sequence.
keras.preprocessing.sequence.pad_sequences(sequences, maxlen=None, dtype='int32', padding='pre', truncating='pre', value=0.0)
I might need to convert my sequence data into one hot vectors first and then zero pad it. For that I tried to use Tokanizeas follows.
tk = Tokenizer(nb_words=?, split=?)
But then, what should be the split value and nb_words as my sequence data doesn't have any space? How to use it for character based one hot?
MY overall goal is to zero pad my sequences and convert it to one hot before I feed it into RNN.
So i came across a way to do by using Tokenizer first and then pad_sequences to zero pad my sequence in the start as follows.
from keras.preprocessing.text import Tokenizer
tokenizer = Tokenizer(char_level=True)
tokenizer.fit_on_texts(X_train_orignal)
sequence_of_int = tokenizer.texts_to_sequences(X_train_orignal)
This gives me the output as follows.
[[3, 1, 4, 2, 3, 5, 1, 6, 2, 1, 4, 1, 7, 5, 1, 2, 1, 1, 2, 1, 6, 1, 7],
[3,
1,
4,
2,
3,
5,
1,
6,
2,
1,
4,
1,
7,
5,
1,
2,
1,
4,
1,
7,
5,
1,
2,
1,
6,
1,
7],
[3, 1, 4, 2, 3, 5, 1, 6, 2, 1, 1, 2, 1, 1, 4, 2, 1, 6, 1, 7, 5, 1, 7],
[3, 1, 4, 2, 3, 5, 1, 6, 2, 1, 1, 4, 2, 1, 1, 2, 1, 6, 1, 7, 5, 1, 7],
[3,
1,
6,
2,
1,
4,
1,
2,
1,
4,
1,
2,
1,
6,
5,
8,
10,
11,
9,
4,
8,
3,
12,
9,
5,
2,
3,
5,
8,
10,
11,
9,
4,
8,
3,
12,
9,
5,
2,
3]]
Now I do not understand why it is giving sequence_of_int[1], sequence_of_int[4] output in column format?
After getting the tokens, I applied the pad_sequences as follows.
seq=keras.preprocessing.sequence.pad_sequences(sequence_of_int, maxlen=None, dtype='int32', padding='pre', value=0.0)
and it gives me the output as follows.
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 3, 1, 4, 2, 3, 5, 1, 6, 2, 1, 4, 1, 7, 5, 1,
2, 1, 1, 2, 1, 6, 1, 7],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 4,
2, 3, 5, 1, 6, 2, 1, 4, 1, 7, 5, 1, 2, 1, 4, 1,
7, 5, 1, 2, 1, 6, 1, 7],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 3, 1, 4, 2, 3, 5, 1, 6, 2, 1, 1, 2, 1, 1, 4,
2, 1, 6, 1, 7, 5, 1, 7],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 3, 1, 4, 2, 3, 5, 1, 6, 2, 1, 1, 4, 2, 1, 1,
2, 1, 6, 1, 7, 5, 1, 7],
[ 3, 1, 6, 2, 1, 4, 1, 2, 1, 4, 1, 2, 1, 6, 5, 8,
10, 11, 9, 4, 8, 3, 12, 9, 5, 2, 3, 5, 8, 10, 11, 9,
4, 8, 3, 12, 9, 5, 2, 3]], dtype=int32)
Then after that, I converted it into one hot as follows.
one_hot=keras.utils.to_categorical(seq)
I'm developping a simple algorithm to detect several facial expressions (happiness, sadness, anger...). I'm based on this paper to do that. I'm preprocessing before to apply LBP uniform operator dividing the normalized image into 6x6 regions as shown in the example below:
By applying uniform LBP 59 feats are extracted for each region, so finally I have 2124 feats by image (6x6x59). I think it's a too large number of feats when I have about 700 images to train a model. I have read that's not good to get a good precission. My question is how can I reduce the dimension of the feats or another technique to improve the precision of the algorithm.
A straightforward way to reduce feature dimensionality - and increase robustness at the same time - would be using rotation-invariant uniform patterns. For a circular neighbourhood of radius and formed by pixels, the texture descriptor represents each region through 10 features. Thus dimensionality is reduced from 2124 to 6 × 6 × 10 = 360.
PCA can help to reduce the size of descriptor without loosing important information. Just google "opencv pca example".
Another helpful thing is to add rotation invariance to your uniform lbp features. This will improve the precision as well as dramatically decrease size of descriptor from 59 to 10.
static cv::Mat rotate_table = (cv::Mat_<uchar>(1, 256) <<
0, 1, 1, 3, 1, 5, 3, 7, 1, 9, 5, 11, 3, 13, 7, 15, 1, 17, 9, 19, 5, 21, 11, 23,
3, 25, 13, 27, 7, 29, 15, 31, 1, 33, 17, 35, 9, 37, 19, 39, 5, 41, 21, 43, 11,
45, 23, 47, 3, 49, 25, 51, 13, 53, 27, 55, 7, 57, 29, 59, 15, 61, 31, 63, 1,
65, 33, 67, 17, 69, 35, 71, 9, 73, 37, 75, 19, 77, 39, 79, 5, 81, 41, 83, 21,
85, 43, 87, 11, 89, 45, 91, 23, 93, 47, 95, 3, 97, 49, 99, 25, 101, 51, 103,
13, 105, 53, 107, 27, 109, 55, 111, 7, 113, 57, 115, 29, 117, 59, 119, 15, 121,
61, 123, 31, 125, 63, 127, 1, 3, 65, 7, 33, 97, 67, 15, 17, 49, 69, 113, 35,
99, 71, 31, 9, 25, 73, 57, 37, 101, 75, 121, 19, 51, 77, 115, 39, 103, 79, 63,
5, 13, 81, 29, 41, 105, 83, 61, 21, 53, 85, 117, 43, 107, 87, 125, 11, 27, 89,
59, 45, 109, 91, 123, 23, 55, 93, 119, 47, 111, 95, 127, 3, 7, 97, 15, 49, 113,
99, 31, 25, 57, 101, 121, 51, 115, 103, 63, 13, 29, 105, 61, 53, 117, 107, 125,
27, 59, 109, 123, 55, 119, 111, 127, 7, 15, 113, 31, 57, 121, 115, 63, 29, 61,
117, 125, 59, 123, 119, 127, 15, 31, 121, 63, 61, 125, 123, 127, 31, 63, 125,
127, 63, 127, 127, 255
);
// the well known original uniform2 pattern
static cv::Mat uniform_table = (cv::Mat_<uchar>(1, 256) <<
0,1,2,3,4,58,5,6,7,58,58,58,8,58,9,10,11,58,58,58,58,58,58,58,12,58,58,58,13,58,
14,15,16,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,17,58,58,58,58,58,58,58,18,
58,58,58,19,58,20,21,22,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,
58,58,58,58,58,58,58,58,58,58,58,58,23,58,58,58,58,58,58,58,58,58,58,58,58,58,
58,58,24,58,58,58,58,58,58,58,25,58,58,58,26,58,27,28,29,30,58,31,58,58,58,32,58,
58,58,58,58,58,58,33,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,34,58,58,58,58,
58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,
58,35,36,37,58,38,58,58,58,39,58,58,58,58,58,58,58,40,58,58,58,58,58,58,58,58,58,
58,58,58,58,58,58,41,42,43,58,44,58,58,58,45,58,58,58,58,58,58,58,46,47,48,58,49,
58,58,58,50,51,52,58,53,54,55,56,57
);
static cv::Mat rotuni_table = (cv::Mat_<uchar>(1, 256) <<
0, 1, 1, 2, 1, 9, 2, 3, 1, 9, 9, 9, 2, 9, 3, 4, 1, 9, 9, 9, 9, 9, 9, 9, 2, 9, 9, 9,
3, 9, 4, 5, 1, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 2, 9, 9, 9, 9, 9, 9, 9,
3, 9, 9, 9, 4, 9, 5, 6, 1, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 2, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
3, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 5, 9, 6, 7, 1, 2, 9, 3, 9, 9, 9, 4, 9, 9, 9, 9,
9, 9, 9, 5, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 6, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 7, 2, 3, 9, 4,
9, 9, 9, 5, 9, 9, 9, 9, 9, 9, 9, 6, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 7,
3, 4, 9, 5, 9, 9, 9, 6, 9, 9, 9, 9, 9, 9, 9, 7, 4, 5, 9, 6, 9, 9, 9, 7, 5, 6, 9, 7,
6, 7, 7, 8
);
static void hist_patch_uniform(const Mat_<uchar> &fI, Mat &histo,
int histSize, bool norm, bool rotinv)
{
cv::Mat ufI, h, n;
if (rotinv) {
cv::Mat r8;
// rotation invariant transform
cv::LUT(fI, rotate_table, r8);
// uniformity for rotation invariant
cv::LUT(r8, rotuni_table, ufI);
// histSize is max 10 bins
} else {
cv::LUT(fI, uniform_table, ufI);
}
// the upper boundary is exclusive
float range[] = {0, (float)histSize};
const float *histRange = {range};
cv::calcHist(&ufI, 1, 0, Mat(), h, 1, &histSize, &histRange, true, false);
if (norm)
normalize(h, n);
else
n = h;
histo.push_back(n.reshape(1, 1));
}
The input is your CV_8U grey-scaled patch (one of those rects). The out is the rotation invariant, uniform, normalized reshaped histogram (1 line). Then you concat your patches histograms into the face descriptor. You will have 6*6*10 = 360. This is good by itself but with pca you can make it 300 or less without loosing important information and even improving the quality of detection because removed dimensions (let's say with variances less than 5%) not just occupy space but also contain mostly the noise (coming from, for example, gaussian noise from the sensor).
Then you can compare this concat histogram with the bank of faces or using svm (rbf kernel fits better). If you do it correctly, then predict for one face should not take more than 1-15ms (5 ms on my iphone7).
Hope this helps.
I want to sort the result here my code:
def games = Game.withCriteria() {
eq('season', currentSeason)
eq('competition', competition)
round {
ge('roundNr', startRound.roundNr)
}
or {
round {
le('roundNr', currentRound.roundNr)
}
gt('state', Game.STATE_NOT_STARTED)
}
order 'date', 'asc'
}
def sortGames = games.asList().sort(games.round.sorting)
The query works fine, i want to sort them.
Error:
Error 500: Executing action [recalcCompetitionTable] of controller [at.ligaportal.CompetitionController] caused exception: groovy.lang.MissingMethodException: No signature of method: java.util.ArrayList.sort() is applicable for argument types: (java.util.ArrayList) values: [[1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 2, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 14, 15, 16, 16, 16, 16, 16, 16, 16, 16]] Possible solutions: sort(), sort(java.util.Comparator), sort(groovy.lang.Closure), wait(), size(), size()
Servlet: grails
URI: /ligaportal/grails/competition/recalcCompetitionTable.dispatch
Exception Message: No signature of method: java.util.ArrayList.sort() is applicable for argument types: (java.util.ArrayList) values: [[1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 2, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 14, 15, 16, 16, 16, 16, 16, 16, 16, 16]] Possible solutions: sort(), sort(java.util.Comparator), sort(groovy.lang.Closure), wait(), size(), size()
Caused by: No signature of method: java.util.ArrayList.sort() is applicable for argument types: (java.util.ArrayList) values: [[1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 2, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 14, 15, 16, 16, 16, 16, 16, 16, 16, 16]] Possible solutions: sort(), sort(java.util.Comparator), sort(groovy.lang.Closure), wait(), size(), size()
Class: ApiAuthenticationFilter
At Line: [77]
where is my Problem?
I got the Games via withCriteria() method, sort by date.
And all games have rounds , and now i want to sort them by the sorting of the rounds.
5 Games have 1 Round and alle Games have dates.
Round 1 = 18.09.2012 - 25.09.2012 (5 Games)
Round 2 = 26.09.2012 - 31.09.2012 (4 Games) and one Game plays later
5.11.2012
Round 3 = 1.10.2012 - 7.10.2013
And my problem is, i sum up the round with a each and previous round.
Roudn 1 = 3,3,3,1,1 Points
Round 2 = 6,4,4,2,1 Points -> 1 Games later
Round 3 = 9,7,7,4,3 Pounts
Round 2 = 6,4,4,4,1 (after game)
Round 4 = here i cant go back to round 4 -> the one game is lost!
And now i want to order the games via game.round.sorting
This is my problem.
Thank you!
The Solution
def bySorting = new Comparator() {
int compare(a,b) { a.round.sorting <=> b.round.sorting }
}
games.sort(bySorting)
You don't need to call sort()on games due to the result of your criteria is already sorted.
Instead sorting by date : order 'date', 'asc' you could add your desired property here.
If you really need to sort the result you have to pass an Comparator or a Closure to sort():
def sortGames = games.asList().sort{it.round.sorting}
You should provide some more information. Looks like sorting is an ArrayList - how should the games be sorted?
You should be able to sort by round as part of the initial database query by using createAlias in your criteria:
def games = Game.withCriteria() {
createAlias('round', 'rnd')
eq('season', currentSeason)
eq('competition', competition)
ge('rnd.roundNr', startRound.roundNr)
or {
le('rnd.roundNr', currentRound.roundNr)
gt('state', Game.STATE_NOT_STARTED)
}
order 'rnd.sorting', 'asc'
}
This way you don't need to do any sorting of the returned list in Groovy.
However, if you do want to sort an existing list of games by their round.sorting value then you almost had the right syntax. What you actually need is:
def sortGames = games.sort { it.round.sorting }
The Groovy sort method expects a closure that returns the sort key, whereas your attempt was passing a list of all the sort keys for all the games.
From your code it is not clear what games.round.sorting does.
But have a look at the following links to learn more about sorting lists in groovy:
Custom list sorting
Groovy OrderBy
Or google for Groovy Comparator to find other examples and descriptions.