I've got a photo taken about 120 years ago of a street near to where I live.
However hard I try I can't seem to get the same perspective if I photograph the same scene from roughly the same place using my current camera.
My attempts at using photoshop's image transformation tools to try to match them up aren't that more succesful.
Does anyone have any hints or pointers on how to proceeed.
My aim is to fade the old image into the new one as part of a short video.
Thanks in advance
I have multiple camera in different points.
I have their position and rotation as $ (x,y,z)$ , $ (\alpha,\beta,\gamma)$ or $ ( roll, pitch, yaw)$ .
And I have output like this :
Feed from camera-1, I know length of yellow and green.
How can I calculate position of the object, in this case head in 3D space.
I looked up a tutorial and created a function based upon its script. It’s essentially used so I can select dependent variables that’s a subset of a data frame. It runs but it is very very slow.
How would I flatten a nested for-loop such as this?
I tried implementing an enumerate version but it did not work. Ideally I’d like the complexity to be linear, currently, it’s at 2^n. I’m not sure, how I can flatten the nested for loop, such that I can append the results of a function to a list.
def BestSubsetSelection(X,Y, plot = True): k = len(X.columns) RSS_list =  R_squared_list =  feature_list =  numb_features =  # Loop over all possible combinations of k features for k in range(1, len(X.columns) + 1): # Looping over all possible combinations: from 11 choose k for combo in itertools.combinations(X.columns,k): # Store temporary results temp_results = fit_linear_reg(X[list(combo)],Y) # Append RSS to RSS Lists RSS_list.append(temp_results) # Append R-Squared TO R-Squared list R_squared_list.append(temp_results) # Append Feature/s to Feature list feature_list.append(combo) # Append the number of features to the number of features list numb_features.append(len(combo))
A copy of the full implementation can be found here: https://github.com/melmaniwan/Elections-Analysis/blob/master/Implementing%20Subset%20Selections.ipynb
Is there a standard reference for understanding sentential, first and higher order logics from a categorical perspective?
I’m close to knowing enough $ 1$ /$ 2$ /internal category theory to tackle the Joyal-Tierney Galois theorem for toposes and the Borceux-Janelidze generalization for internal precategories to give some idea of my knowledge base. I’ve heard that category theory can model all of these logics as the internal logic of an appropriate (possibly higher) category, and I was looking for a reference that builds up logic from this perspective for someone who has never explicitly read a logic textbook.
For the record I have “A Course in Mathematical Logic” by Bell and Machover ordered and in the mail; I fully intend to take the classical route up through logic as well, but I was curious about any categorical cheat codes along the way.
It seems (naively) like type theory might be the answer here, and I would be open to suggestions in that direction, but I am currently unfamiliar with the inner workings (or general moral) of type theory.
Perspective through CNS (canvas, paper, screen) plane
View post on imgur.com
I guess im asking for something similair to code review but for trigonometry
Let’s say that I have a class representing the Neural Network. The neural network is composed of three bigger units: a subpart_1, subpart_2 and subpart_3, being called in such a way, that the output of one part is an input to the next one. The parts are itself Neural Networks with the same basic interface. In my current approach, each of this part is created inside the constructor, the __init__method of my class, and I am passing in the configuration parameters. It inherits from some framework NeuralNetwork class which gives me access to some common interface like forward/backward methods. It looks like this:
class NeuralNetwork(framework.NeuralNetworkBase): def __init__(self, subp_1_params, subp_2_params, subp_3_params) super(...) self.subpart_1 = Subpart1Class(subp_1_params) self.subpart_2 = Subpart2Class(subp_2_params) self.subpart_3 = Subpart3Class(subp_3_params) . . . def forward(self, input): <use the subparts on the data in some way>
You can imagine the SubpartXClasses being implemented in a similar manner. Now, I have been recently reading more on Unit Testing and Unit Testing-friendly design and in particular I have stumbled upon the blog post:
in which the author claims, that unless the fields of the class are not plain data structures, they should not be created inside the constructor and instead passed as the constructor arguments to allow for the maximally decoupled and unit testing-friendly design. The tone of that posts is really strict but it is also quite old. So now my question: In a scenario like this, would it be a straight up better idea to create the objects externally, through some factory, and pass a ready and configured objects into the constructor to register them to my NeuralNetwork class? From my point of view, the current approach seems more natural (actually the framework I am using also promotes this approach), but after reading that blog posts I started having some doubts.
I have been looking for a project for earning Bitcoins for a long time and stopped at flymining. Why this particular company? The main thing is the stability of the mining, plus fast timely withdrawal. The company is young and offers good conditions. https://flymining.cloud/?promocode=4G0OPX
I would like to have a website that is loaded fast from any point in the world. From my understanding, you need to take advantage of the data center “regions” at places like Google Cloud or AWS. That’s as far as my understanding goes.
What is missing is how exactly to implement it (at a high/diagram level here, not at the code level).
For example, on Google Cloud for a specific “project” you have the choice of IPv4 or IPv6, of “Premium” vs. “Standard” (“Traffic traverses Google’s high quality global backbone, entering and exiting at Google edge peering points closest to the user.” vs. “Traffic enters and exits the Google network at a peering point closest to the Cloud region it’s destined for or originated in.”), and of Global vs. Regional. If you select IPv6 you are limited to Premium. If you select Global on IPv4 you are limited to Premium. I am not sure how this works, not at the level of detail of Google’s specific system, but what generally is going on here. I don’t see how a “global” IP address can be better than a regional one, since the regional one is closer to the request source.
On AWS, they don’t have a “Global” option, all IP addresses are IPv4 and they are regional.
That was just some background for the main question.
My question is how to architect a system to take advantage of regional data centers. Just generally, at the IP level. I am wondering how it goes from my domain to the regional IP address. Or if it is like Google Cloud’s “Global” IP address, how you could then integrate regions into it. Or if that’s backwards, how to conceptualize of this. I saw the diagram below, but it only explains how a domain is mapped to a single region-independent IP address. I don’t see how regions are actually implemented / come into play.
Basically I would like to know at a high level how I should organize my IP addresses and servers to take advantage of regional data centers. So far my thinking is of having servers in different regions with their own copies of data. But then I get lost when thinking about IP addresses and domains. If I use the AWS model, it seems I reserve an IP per entrypoint server per region. But then I don’t see how the domain name figures out which IP/region to select. If I use the global Google Cloud model, I don’t see how I can add regional servers.
How’s the best way to deal with (how to model – and later in the design where to validate) referential integrity rules for noSQL databases, from the perspective of DDD?
(specific examples for MongoDB would be a plus)