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please help me to see what wrong with my code?
s = NDSolve({(m*g*
Sin((Theta)(t))) - (m*(r''(t) - r (((Theta)'(t))^2))) ==
k (r(t) - 14), g*Cos((Theta)(t)) ==
r(t) (Theta)''(t) + 2*r'(t) (Theta)'(t), (Theta)'(0) == 0,
r'(0) == 0}, {r, (Theta)}, {t, 0, 60}, {k, 0, 2000})
i want to solve this two equation to find equation of r(t). I’ve already set up m and g
As I understand it, the process of K means clustering takes a set of sample points with k arbitrary centroids and uses Euclidean distance to classify the points closest to centroids to k groups.
What I am unable to understand is a point in the cartesian plane has only an x and y coordinate and so, amongst a given dataset we can only chose 2 independent variables and plot the points and proceed with the algorithm. However, there might be many more independent variables which could influence classification, for example, if we are trying to classify dogs based on their breeds using physical attributes such as size of ears, radius of eyes, body weight, length of legs, lifespan and so on. I’m not sure how this problem is resolved in K clustering.
Are the two variables with maximum information gain considered or are the points plotted in an n-dimensional space where each axis defines each attribute.
Could someone provide clarity on this issue. Thanks for any help
Is there a utility or sdk out there which will allow me to report differences in the time it takes for an action to be completed in a way that takes into account the system attributes (Ram, Storage Type(s), CPU Speed, etc) so that expected completion time can be estimated on another system ?
How can we in MS flow control the execution of other flows?
To be more specific:
How can we do the followings in a flow:
1 -Check the status of other flows? e,g, running, idle
2- If they are running, for how long they are running?
3- Turn the other flows on or off?
4-a bit greedy, but can we check the value of a variable in other flows if they are in a running state? (e.g. Can we check the variable 'var_a' of flow B which is in the running state while we are in flow A (no child-parent relation between these flows)?
let t: r2 to r2 be a linear map such that t(1,0)=u,t(0,1)=v where u and v are two linearly independent vectors.describe the image under t of the rectangle whose corners are at (0,0),(0,1),(3,1),(3,0)
I’ve got several Linux hosts (CentOS 7, x86_64) configured with NIC teaming for redundancy and load balancing. Two 10 GBit ports on the host are connected to two 10 GBit switches and configured to listen to the same IP. The problem we have is that if the switches are independent, we see about 4% packet loss, which is very high. If the switches are “stacked” to operate as one (or, for test purposes, if both cables are connected to the SAME switch), the packet loss goes away. The team is configured in load balance mode; we are not using link aggregation/LACP.
Has anyone else encountered this and/or know if there’s some easy configuration change that eliminates this issue. Obviously, the ideal for redundancy would be to have separate, totally independent switches, so that if a switch fails, connectivity remains.
Here’s the team config (IPs and domain redacted):
PROXY_METHOD=none
BROWSER_ONLY=no
BOOTPROTO=none
DEFROUTE=yes
IPV4_FAILURE_FATAL=no
IPV6INIT=no
NAME=team-main-b1
UUID=a919c3ac-903a-424d-8aa7-9feaf700e577
DEVICE=team-main-b1
ONBOOT=yes
DEVICETYPE=Team
TEAM_CONFIG="{"runner": {"name": "loadbalance", "tx_hash": ("eth", "ipv4", "ipv6"), "tx_balancer": {"name": "basic"}}}"
RES_OPTIONS=single-request-reopen
Let’s say we have independent events E1 and E2. Does this also mean that !E1 and E2 are independent? And if not can someone give me an example where !E1 and E2 are dependent?
I am trying to design a parallel scheduling algorithm based on a constraint graph $G=(V,E)$ in which each node represents a task and each edge $e=(v_1, v_2)$ signifies, that tasks $v_1$ and $v_2$ can not be executed in parallel. Each task is executed exactly once, so the problem is finding “good” independent sets $V_i$, so that
$$
bigcup_{i=1}^{k} V_i = V\
$$
with all independent sets $V_i, V_j$ being pairwise disjoint. Since MaxIS is NP-Hard my approach would be solving MIS repeatedly (finding some maximal independent set, removing those vertices and start again until the Graph is empty). I know that in the worst case of $G$ being a clique this approach would yield $n$ iterations, however in my instance i would have the guarantee that the number of neighbors of each node would be upper-bound by $c ll |V|$.
My question is: Given such a $c$ is there any upper bound on the number of necessary steps $k$?
Consider the following graph:
None of the vertices has degree 1, so we go straight to step 5. Your algorithm would return 5 as the maximum independent set size, as both $L$ and $R$ have 5 vertices. However, the actual maximum independent set is ${1, 2, 3, 6, 7, 8}$, which has 6 vertices.
So basically the scenario is this:
I’m supposed to add code and functionality to an Android project. There is one BaseActivity which all other Activities extend that provides common functionality. I know best practice according to Google is to have one Base Activity and multiple Fragments because those are “cheaper” than many Activities. My deadline does not allow me to make fundamental changes to the existing code and quite frankly it is hard to get a grasp of how everything works together since in my opinion the code is not very good. My code is mostly independent from the existing code so I wondered whether or not I should create my own Activity including multiple Fragments or follow the current architecture to not confuse future devs that have to work with the code.
Would you people recommend me?
