segment | segment text based on frequencies and the Viterbi algorithm

I need to find the intersection of two arrays and print out the number of elements in the intersection of the two arrays. I must also account for any duplicate elements in both the arrays. So, I decide to take care of the duplicate elements by converting the two arrays into sets and then take the intersection of both the sets. However, I encounter a segmentation fault when I run my code. I'm not sure where this occurs, any way to fix this?

I am crunching large amounts of data without a hitch until I added more data. The results are written to file as strings, but I received this error message and I am unable to find programming error after combing my codes for 2 days; my codes have been working fine before new data were added.

In my iOS app "Progression" there is rarely a crash (1 crash in ~1000+ Sessions) I am currently not able to fix. The message is

Progression: protocol witness for TrainingSetSessionManager.update(object:weight:reps:) in conformance TrainingSetSessionDataManager + 40

This crash points me to the following method:

I need a way to force the compaction of the __consumer_offsets topic. In a test environment I tried to delete the file cleaner-offset-checkpoint and then kafka deleted many segments as you can see below. Is it safe to delete this file in a production environment?

Before removing cleaner-offset-checkpoint:

I have a dataframe:

I am solving this problem on dynamic array in which input first line contains two space-separated integers,n, the size of arr to create, and q, the number of queries, respectively. Each of the q subsequent lines contains a query string,queries[i]. it expects to return int[]: the results of each type 2 query in the order they are presented.

i tried to attempt as below and my code seems fine to me but it gives segmentation fault error. please help me where I am getting conceptually wrong. thanks.

problem: Declare a 2-dimensional array,arr , of n empty arrays. All arrays are zero indexed. Declare an integer,last answer , and initialize it to zero.

There are 2 types of queries, given as an array of strings for you to parse:

Query: 1 x y

Let idx=((queries[i][1]^last_answer)%n);. Append the integer y to arr[idx].

Query: 2 x y

Let idx=((queries[i][1]^last_answer)%n);. Assign last_answer=arr[idx][queries[i][2]%(arr[idx].size())] . Store the new value of last_answer to an answers array.

input: 2 5

1 0 5

1 1 7

1 0 3

2 1 0

2 1 1

output:

7

3

The problem is the following: I got a png file : example.png

• that I filter using chan vese of skimage.segmentation.chan_vese

  • It's return a png file in black and white.

• i detect segments around my new png file with cv2.ximgproc.createFastLineDetector()

  • it's return a list a segment

But the list of segments represent disjoint segments.

I use two naive methods to polygonize this list of segment:

-It's seems that cv2.ximgproc.createFastLineDetector() create a almost continuous list so I just join by creating new segments:

I would like to find minimum distance of each voxel to a boundary element in a binary image in which the z voxel size is different from the xy voxel size. This is to say that a single voxel represents a 225x110x110 (zyx) nm volume.

Normally, I would do something with scipy.ndimage.morphology.distance_transform_edt (https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.morphology.distance_transform_edt.html) but this gives the assume that isotropic sizes of the voxel:

I wanted to perform a simple calculation of the covariance within a more complex flask app. Below I created a minimal random example without flask (which is actually working) of the calculation causing the problems (in the flask/waitress setup).