enter image description here
cov_xx = (1/N)*np.sum(data_normalized[:,0]*data_normalized[:,0])
cov_xy = (1/N)*np.sum(data_normalized[:,0]*data_normalized[:,1])
cov_yx = (1/N)*np.sum(data_normalized[:,1]*data_normalized[:,0])
cov_yy = (1/N)*np.sum(data_normalized[:,1]*data_normalized[:,1])
print_covariance_matrix()
Hi guys, this is my first post here, I hope it's not an illegal question. My question is as you can see in the picture and my code is as below but I still get the wrong answer. Where is my mistake?
Related
Using Binance Futures API I am trying to get a proper form of my position regarding cryptocurrencies.
Using the code
from binance_f import RequestClient
request_client = RequestClient(api_key= my_key, secret_key=my_secet_key)
result = request_client.get_position()
I get the following result
[{"symbol":"BTCUSDT","positionAmt":"0.000","entryPrice":"0.00000","markPrice":"5455.13008723","unRealizedProfit":"0.00000000","liquidationPrice":"0","leverage":"20","maxNotionalValue":"5000000","marginType":"cross","isolatedMargin":"0.00000000","isAutoAddMargin":"false"}]
The type command indicates it is a list, however adding at the end of the code print(result) yields:
[<binance_f.model.position.Position object at 0x1135cb670>]
Which is baffling because it seems not to be the list (in fact, debugging it indicates object of type Position). Using PrintMix.print_data(result) yields:
data number 0 :
entryPrice:0.0
isAutoAddMargin:True
isolatedMargin:0.0
json_parse:<function Position.json_parse at 0x1165af820>
leverage:20.0
liquidationPrice:0.0
marginType:cross
markPrice:5442.28502271
maxNotionalValue:5000000.0
positionAmt:0.0
symbol:BTCUSDT
unrealizedProfit:0.0
Now it seems like a JSON format... But it is a list. I am confused - any ideas how I can convert result to a proper DataFrame? So that columns are Symbol, PositionAmt, entryPrice, etc.
Thanks!
Your main question remains as you wrote on the header you should not be confused. In your case you have a list of Position object, you can see the structure of Position in the GitHub of this library
Anyway to answer the question please use the following:
df = pd.DataFrame([t.__dict__ for t in result])
For more options and information please read the great answers on this question
Good Luck!
you can use that
df = pd.DataFrame([t.__dict__ for t in result])
klines=df.values.tolist()
open = [float(entry[1]) for entry in klines]
high = [float(entry[2]) for entry in klines]
low = [float(entry[3]) for entry in klines]
close = [float(entry[4]) for entry in klines]
I have trained a Word2Vec model and I am trying to use it.
When I input the most similar words of ‘动力', I got the output like this:
动力系统 0.6429724097251892
驱动力 0.5936785936355591
动能 0.5788494348526001
动力车 0.5579575300216675
引擎 0.5339343547821045
推动力 0.5152761936187744
扭力 0.501279354095459
新动力 0.5010953545570374
支撑力 0.48610919713974
精神力量 0.47970670461654663
But the problem is that if I input model.wv.similarity('动力','动力系统') I got the result 0.0, which is not equal with
0.6429724097251892
what confused me more was that when I got the next similarity of word '动力' and word '驱动力', it showed
3.689349e+19
So why ? Did I make misunderstanding with the similarity? I need someone to tell me!!
And the code is:
res = model.wv.most_similar('动力')
for r in res:
print(r[0],r[1])
print(model.wv.similarity('动力','动力系统'))
print(model.wv.similarity('动力','驱动力'))
print(model.wv.similarity('动力','动能'))
output:
动力系统 0.6429724097251892
驱动力 0.5936785936355591
动能 0.5788494348526001
动力车 0.5579575300216675
引擎 0.5339343547821045
推动力 0.5152761936187744
扭力 0.501279354095459
新动力 0.5010953545570374
支撑力 0.48610919713974
精神力量 0.47970670461654663
0.0
3.689349e+19
2.0
I have written a function to replace the model.wv.similarity method.
def Similarity(w1,w2,model):
A = model[w1]; B = model[w2]
return sum(A*B)/(pow(sum(pow(A,2)),0.5)*pow(sum(pow(B,2)),0.5)
Where w1 and w2 are the words you input, model is the Word2Vec model you have trained.
Using the similarity method directly from the model is deprecated. It has a bit extra logic in it that performs vector normalization before evaluating the result.
You should be using vw directly, because as stated in their documentation, for the word vectors it is of non importance how they were trained so they should be looked as independent structure, the model is just the means to obtain it.
Here is short discussion which should give you starting points if you want to investigate further.
It may be an encoding issue, where you are not actually comparing the same tokens.
Try the following, to see if it gives results closer to what you expect.
res = model.wv.most_similar('动力')
for r in res:
print(r[0],r[1])
print(model.wv.similarity('动力', res[0][0]))
print(model.wv.similarity('动力', res[1][0]))
print(model.wv.similarity('动力', res[2][0]))
If it does, you could look further into why the model might be reporting strings which print as 动力系统 (etc), but don't match your typed-in-code string literals like '动力系统' (etc). For example:
print(res[0][0]=='动力系统')
print(type(res[0][0]))
print(type('动力系统'))
Trying to get a partial match in a list, from a user input.
I am trying to make a simple diagnostic program. The user inputs their ailment and the program will output a suggested treatment.
print("What is wrong with you?")
answer=input()
answer=answer.lower()
problem=""
heat=["temperature","hot"]
cold=["freezing","cold"]
if answer in heat:
problem="heat"
if answer in cold:
problem="cold"
print("you have a problem with",problem)
I can get it to pick an exact match from the list but I want it to find partial matches from my input. For example if the user types they are "too hot".
Try the code below. The key is the split() method.
answer = input('What is wrong with you?')
answer = answer.lower()
heat = ['temperature', 'hot']
cold = ['freezing', 'cold']
for word in answer.split():
if word in heat:
problem = 'heat'
if word in cold:
problem = 'cold'
print('you have a problem with', problem)
I would recommend you use something like this which might be a bit more "pythonic"
answer = input()
cold = ["freezing", "cold"]
if any(answer in c for c in cold):
problem = "cold"
Hello I am a beginner in Prolog and i have stuck in the following problem.
Here it goes , I have a "database" which gives me information about the school schedule
something like this :
school(NameOfTeacher,([(Course,Day) ......]).
When asking the following
-? find(staff(NameOfTeacher,Course),Day)
the answer should be Day = (the day the course takes place). I manage to take an answer like Day = (Course,Day) but that it not what I want. Has anyone any idea of how to do this? Thank you in advance.
Remember that Prolog unification is a kind of bi-directional pattern matching, so you can use it to both create and decompose data structures:
?- Pair = (maths,monday), (_,Day) = Pair.
Pair = (maths, monday)
Day = monday
Yes
I am a beginner programmer and I am trying to access pixel data from a picture using python. I want to eventually get the pixel data into an array. I searched the web for the code of how to do this and this is what I got:
from PIL import Image
im = Image.open("C:/Users/Owner/Desktop/bw.png")
pix = im.load()
print pix[x,y]
pix[x,y] = value
It seems to work fine until I get to the print[x,y] line. I get an error saying "NameError: name 'x' is not defined". I have downloaded PIL 1.1.7.
Can anyone lend me a helping hand?
Uh, you didn't define x, y, or value...maybe try defining those first? What pixel did you want to access?
im = Image.open("C:/Users/Owner/Desktop/bw.png")
x, y = 1, 2 #sample coordinates
print im.getpixel((x, y))
that should work, note that to getpixel method you pass one argument - a tuple