def FE_dirichlet(u_j, mx, mt, lmbda):
# Creating matrix
A_FE = np.zeros(shape=(mx - 1, mx - 1))
np.fill_diagonal(A_FE, 1 - 2 * lmbda)
np.fill_diagonal(A_FE[1:], lmbda)
np.fill_diagonal(A_FE[:, 1:], lmbda)
u_jp1 = np.zeros(u_j.size)
# setting boundary conditions, constant for now. can change to function
p_j = 0.0002
q_j = 0.0003
bound = np.zeros(shape=(9, 1))
bound[0] = p_j
bound[-1] = q_j
# Solve the PDE: matrix multiplications
for i in range(0, mt):
u_jp1[1:-1] = np.matmul(A_FE, transpose(
u_j[1:-1])) + lmbda * transpose(bound)
# Boundary conditions
# u_jp1[0] = p_j ; u_jp1[mx] = q_j
# Save u_j at time t[j+1]
u_j = u_jp1
return u_j
def apply_along_axis(func1d, axis, arr, flux, *args, **kwargs):
# handle negative axes
arr = asanyarray(arr)
flux = asanyarray(flux)
nd = arr.ndim
axis = normalize_axis_index(axis, nd)
# arr, with the iteration axis at the end
in_dims = list(range(nd))
inarr_view = transpose(arr, in_dims[:axis] + in_dims[axis + 1:] + [axis])
flux_view = transpose(flux, in_dims[:axis] + in_dims[axis + 1:] + [axis])
# compute indices for the iteration axes, and append a trailing ellipsis to
# prevent 0d arrays decaying to scalars, which fixes gh-8642
inds = ndindex(inarr_view.shape[:-1])
inds = (ind + (Ellipsis, ) for ind in inds)
# invoke the function on the first item
try:
ind0 = next(inds)
except StopIteration:
raise ValueError(
'Cannot apply_along_axis when any iteration dimensions are 0')
res = asanyarray(func1d(inarr_view[ind0], flux_view[ind0], *args,
**kwargs))
# build a buffer for storing evaluations of func1d.
# remove the requested axis, and add the new ones on the end.
# laid out so that each write is contiguous.
# for a tuple index inds, buff[inds] = func1d(inarr_view[inds])
buff = zeros(inarr_view.shape[:-1] + res.shape, res.dtype)
# permutation of axes such that out = buff.transpose(buff_permute)
buff_dims = list(range(buff.ndim))
buff_permute = (buff_dims[0:axis] +
buff_dims[buff.ndim - res.ndim:buff.ndim] +
buff_dims[axis:buff.ndim - res.ndim])
# matrices have a nasty __array_prepare__ and __array_wrap__
if not isinstance(res, matrix):
buff = res.__array_prepare__(buff)
# save the first result, then compute and save all remaining results
buff[ind0] = res
for ind in inds:
buff[ind] = asanyarray(
func1d(inarr_view[ind], flux_view[ind], *args, **kwargs))
if not isinstance(res, matrix):
# wrap the array, to preserve subclasses
buff = res.__array_wrap__(buff)
# finally, rotate the inserted axes back to where they belong
return transpose(buff, buff_permute)
else:
# matrices have to be transposed first, because they collapse dimensions!
out_arr = transpose(buff, buff_permute)
return res.__array_wrap__(out_arr)
def FE_neumann(u_j, mx, mt, lmbda):
# Creating matrix
A_FE = np.zeros(shape=(mx + 1, mx + 1))
np.fill_diagonal(A_FE, 1 - 2 * lmbda)
np.fill_diagonal(A_FE[1:], lmbda)
np.fill_diagonal(A_FE[:, 1:], lmbda)
u_jp1 = np.zeros(u_j.size)
# setting boundary conditions, constant for now. can change to function
P_j = 0.0002
Q_j = 0.0003
bound = np.zeros(shape=(11, 1))
bound[0] = -P_j
bound[-1] = Q_j
bound = transpose(bound)
# Solve the PDE: matrix multiplications
for i in range(0, mt):
u_jp1 = np.matmul(A_FE, transpose(u_j)) + 2 * deltax * lmbda * bound
# Save u_j at time t[j+1]
u_j = u_jp1[-1]
# Boundary conditions
u_j[0] = P_j
u_j[mx] = Q_j
return u_j
def doTest(dataSetName, classifier):
allData = loadmat('data/' + dataSetName + '.mat')
trainLblsAndData = transpose(allData[dataSetName + '_train'])
testLblsAndData = transpose(allData[dataSetName + '_test'])
trainLabels, trainData = trainLblsAndData[:, 0], trainLblsAndData[:, 1:]
testLabels, testData = testLblsAndData[:, 0], testLblsAndData[:, 1:]
classifier.train(trainData, trainLabels)
mcR = classifier.test(testData, testLabels)
return mcR
def normalizeDataNN(array):
m = transpose(array)
res = np.zeros((len(array[0]), len(array)), dtype=float)
for i in range(len(m)):
maximum = max(m[i])
minimum = min(m[i])
for j in range(len(m[i])):
res[i][j] = float((
(m[i][j] - minimum) / float(maximum - minimum)) - 0.5)
return transpose(res)
def normalizeDataNN(array):
m = transpose(array)
res = np.zeros((len(array[0]), len(array)), dtype=float)
for i in range(len(m)):
maximum = max(m[i])
minimum = min(m[i])
avg = np.average(m[i])
std = np.std(m[i])
for j in range(len(m[i])):
res[i][j] = float(m[i][j] - avg) / std
return transpose(res)
def quote(ticker):
ticker = yf.Ticker(ticker)
quote_df = pd.DataFrame([{
"Symbol": ticker.info["symbol"],
"Name": ticker.info["shortName"],
"Price": ticker.info["regularMarketPrice"],
"Open": ticker.info["regularMarketOpen"],
"High": ticker.info["dayHigh"],
"Low": ticker.info["dayLow"],
"Previous Close": ticker.info["previousClose"],
"Volume": ticker.info["volume"],
"52 Week High": ticker.info["fiftyTwoWeekHigh"],
"52 Week Low": ticker.info["fiftyTwoWeekLow"],
}])
quote_df["Change"] = quote_df["Price"] - quote_df["Previous Close"]
quote_df["Change %"] = quote_df.apply(
lambda x: f'{((x["Change"] / x["Previous Close"]) * 100):.2f}%',
axis="columns",
)
for c in [
"Price",
"Open",
"High",
"Low",
"Previous Close",
"52 Week High",
"52 Week Low",
"Change",
]:
quote_df[c] = quote_df[c].apply(lambda x: f"{x:.2f}")
quote_df["Volume"] = quote_df["Volume"].apply(lambda x: f"{x:,}")
quote_df = quote_df.set_index("Symbol")
quote_data = transpose(quote_df)
return quote_data
def Reccomendation():
movie=Movies.query.filter_by().all()
##Users
Users=['imdb','tmdb','Ayush']
num_users=len(Users)
##user_movies
user_movies=[]
user1=[]
user2=[]
user3=[]
for i in range(1,50):
movie=Movies.query.filter_by(Movie_Id=i).first()
user3.append(movie.User1)
for j in range(num_movies):
user1.append(int(movies_df.iloc[j,18]))
for j in range(num_movies):
user2.append((int(movies_df.iloc[j,13]))/20)
user_movies.append(user1)
user_movies.append(user2)
user_movies.append(user3)
user_movies=np.array(user_movies)
##user_genres
user_genre=np.dot(user_movies,movie_genres)
user_genre=user_genre/(num_users*10)
##user_rating
user_rating=np.dot(user_genre,transpose(movie_genres))
for i in range(1,50):
movie=Movies.query.filter_by(Movie_Id=i).first()
movie.User_Rating=user_rating[2][i-1]
movie=Movies.query.filter_by(reccomend=1).order_by(desc(Movies.User_Rating)).limit(params['num_reccomendations']).all()
return render_template('reccomendation.html',params=params,movie=movie)
def calcN(classKernels, trainLabels):
N = zeros((len(trainLabels), len(trainLabels)))
for i, l in enumerate(unique(trainLabels)):
numExamplesWithLabel = len(where(trainLabels == l)[0])
Idiff = identity(numExamplesWithLabel, Float64) - (1.0 / numExamplesWithLabel) * ones(numExamplesWithLabel, Float64)
firstDot = dot(classKernels[i], Idiff)
labelTerm = dot(firstDot, transpose(classKernels[i]))
N += labelTerm
N = nan_to_num(N)
#make N more numerically stable
#if I had more time, I would train this parameter, but I don't
additionToN = ((mean(diag(N)) + 1) / 100.0) * identity(N.shape[0], Float64)
N += additionToN
#make sure N is invertable
for i in range(1000):
try:
inv(N)
except LinAlgError:
#doing this to make sure the maxtrix is invertable
#large value supported by section titled
#"numerical issues and regularization" in the paper
N += additionToN
return N
def train(self, data, labels):
l = labels.reshape((-1,1))
xy = data * l
H = dot(xy,transpose(xy))
f = -1.0*ones(labels.shape)
lb = zeros(labels.shape)
ub = self.C * ones(labels.shape)
Aeq = labels
beq = 0.0
p = QP(matrix(H),f.tolist(),lb=lb.tolist(),ub=ub.tolist(),Aeq=Aeq.tolist(),beq=beq)
r = p.solve('cvxopt_qp')
r.xf[where(r.xf<1e-3)] = 0
self.w = dot(r.xf*labels,data)
nonzeroindexes = where(r.xf>1e-4)[0]
l1 = nonzeroindexes[0]
self.w0 = 1.0/labels[l1]-dot(self.w,data[l1])
self.numSupportVectors = len(nonzeroindexes)
def FE(u_j, mx, mt, lmbda):
# Creating matrix
A_FE = np.zeros(shape=(mx - 1, mx - 1))
np.fill_diagonal(A_FE, 1 - 2 * lmbda)
np.fill_diagonal(A_FE[1:], lmbda)
np.fill_diagonal(A_FE[:, 1:], lmbda)
u_jp1 = np.zeros(u_j.size)
# Solve the PDE: matrix multiplications
for i in range(0, mt):
u_jp1[1:-1] = np.matmul(A_FE, transpose(u_j[1:-1]))
# Boundary conditions
u_jp1[0] = 0
u_jp1[mx] = 0
# Save u_j at time t[j+1]
u_j = u_jp1
return u_j
def __train__(self, data, labels):
l = labels.reshape((-1,1))
xy = data * l
H = dot(xy,transpose(xy))
f = -1.0*ones(labels.shape)
lb = zeros(labels.shape)
ub = self.C * ones(labels.shape)
Aeq = labels
beq = 0.0
devnull = open('/dev/null', 'w')
oldstdout_fno = os.dup(sys.stdout.fileno())
os.dup2(devnull.fileno(), 1)
p = QP(matrix(H),f.tolist(),lb=lb.tolist(),ub=ub.tolist(),Aeq=Aeq.tolist(),beq=beq)
r = p.solve('cvxopt_qp')
os.dup2(oldstdout_fno, 1)
lim = 1e-4
r.xf[where(r.xf<lim)] = 0
self.w = dot(r.xf*labels,data)
nonzeroindexes = where(r.xf>lim)[0]
l1 = nonzeroindexes[0]
self.w0 = 1.0/labels[l1]-dot(self.w,data[l1])
self.numSupportVectors = len(nonzeroindexes)
def Home():
p_movie=Movies.query.order_by(desc(Movies.popularity)).limit(6).all()
movie=Movies.query.filter_by().all()
##Users
Users=['imdb','tmdb','Ayush']
num_users=len(Users)
##user_movies
user_movies=[]
user1=[]
user2=[]
user3=[]
for i in range(1,50):
movie=Movies.query.filter_by(Movie_Id=i).first()
user3.append(movie.User1)
for j in range(num_movies):
user1.append(int(movies_df.iloc[j,18]))
for j in range(num_movies):
user2.append((int(movies_df.iloc[j,13]))/20)
user_movies.append(user1)
user_movies.append(user2)
user_movies.append(user3)
user_movies=np.array(user_movies)
##user_genres
user_genre=np.dot(user_movies,movie_genres)
user_genre=user_genre/(num_users*10)
##user_rating
user_rating=np.dot(user_genre,transpose(movie_genres))
for i in range(1,50):
movie=Movies.query.filter_by(Movie_Id=i).first()
movie.User_Rating=user_rating[2][i-1]
r_movie=Movies.query.filter_by(reccomend=1).order_by(desc(Movies.User_Rating)).limit(6).all()
t_movie=Movies.query.order_by(desc(Movies.vote_average)).limit(6).all()
return render_template('index.html',params=params,p_movie=p_movie,r_movie=r_movie,t_movie=t_movie)
def logisticRegression(trainData, trainLabels, testData, testLabels):
#adjust the data, adding the 'free parameter' to the train data
trainDataWithFreeParam = hstack((trainData.copy(), ones(trainData.shape[0])[:,newaxis]))
testDataWithFreeParam = hstack((testData.copy(), ones(testData.shape[0])[:,newaxis]))
alpha = 10
oldW = zeros(trainDataWithFreeParam.shape[1])
newW = ones(trainDataWithFreeParam.shape[1])
iteration = 0
trainDataWithFreeParamTranspose = transpose(trainDataWithFreeParam)
alphaI = alpha * identity(oldW.shape[0])
while not array_equal(oldW, newW):
if iteration == 100:
break
oldW = newW.copy()
yVect = yVector(oldW, trainDataWithFreeParam)
r = R(yVect)
firstTerm = inv(alphaI + dot(dot(trainDataWithFreeParamTranspose, r), trainDataWithFreeParam))
secondTerm = dot(trainDataWithFreeParamTranspose, (yVect-trainLabels)) + alpha * oldW
newW = oldW - dot(firstTerm, secondTerm)
iteration += 1
#see how well we did
numCorrect = 0
for x,t in izip(testDataWithFreeParam, testLabels):
if yScalar(newW, x) >= 0.5:
if t == 1:
numCorrect += 1
else:
if t == 0:
numCorrect += 1
return float(numCorrect) / float(len(testLabels))
def Crank_Nicholson(u_j, mx, mt, lmbda):
# Creating matrix
A_cn = np.zeros(shape=(mx - 1, mx - 1))
np.fill_diagonal(A_cn, 1 + lmbda)
np.fill_diagonal(A_cn[1:], -lmbda / 2)
np.fill_diagonal(A_cn[:, 1:], -lmbda / 2)
B_cn = np.zeros(shape=(mx - 1, mx - 1))
np.fill_diagonal(B_cn, 1 - lmbda)
np.fill_diagonal(B_cn[1:], lmbda / 2)
np.fill_diagonal(B_cn[:, 1:], lmbda / 2)
# Set up the solution variables
u_jp1 = np.zeros(u_j.size) # u at next time step
# Solve the PDE: matrix multiplications
for i in range(0, mt):
u_jp1[1:-1] = linalg.solve(A_cn, np.matmul(B_cn, transpose(u_j[1:-1])))
# Save u_j at time t[j+1]
u_j = u_jp1
return u_j
def apply_along_axis(func1d, axis, arr, *args, **kwargs):
"""
Apply a function to 1-D slices along the given axis.
Execute `func1d(a, *args, **kwargs)` where `func1d` operates on 1-D arrays
and `a` is a 1-D slice of `arr` along `axis`.
This is equivalent to (but faster than) the following use of `ndindex` and
`s_`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of indices::
Ni, Nk = a.shape[:axis], a.shape[axis+1:]
for ii in ndindex(Ni):
for kk in ndindex(Nk):
f = func1d(arr[ii + s_[:,] + kk])
Nj = f.shape
for jj in ndindex(Nj):
out[ii + jj + kk] = f[jj]
Equivalently, eliminating the inner loop, this can be expressed as::
Ni, Nk = a.shape[:axis], a.shape[axis+1:]
for ii in ndindex(Ni):
for kk in ndindex(Nk):
out[ii + s_[...,] + kk] = func1d(arr[ii + s_[:,] + kk])
Parameters
----------
func1d : function (M,) -> (Nj...)
This function should accept 1-D arrays. It is applied to 1-D
slices of `arr` along the specified axis.
axis : integer
Axis along which `arr` is sliced.
arr : ndarray (Ni..., M, Nk...)
Input array.
args : any
Additional arguments to `func1d`.
kwargs : any
Additional named arguments to `func1d`.
.. versionadded:: 1.9.0
Returns
-------
out : ndarray (Ni..., Nj..., Nk...)
The output array. The shape of `out` is identical to the shape of
`arr`, except along the `axis` dimension. This axis is removed, and
replaced with new dimensions equal to the shape of the return value
of `func1d`. So if `func1d` returns a scalar `out` will have one
fewer dimensions than `arr`.
See Also
--------
apply_over_axes : Apply a function repeatedly over multiple axes.
Examples
--------
>>> def my_func(a):
... \"\"\"Average first and last element of a 1-D array\"\"\"
... return (a[0] + a[-1]) * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(my_func, 0, b)
array([4., 5., 6.])
>>> np.apply_along_axis(my_func, 1, b)
array([2., 5., 8.])
For a function that returns a 1D array, the number of dimensions in
`outarr` is the same as `arr`.
>>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])
>>> np.apply_along_axis(sorted, 1, b)
array([[1, 7, 8],
[3, 4, 9],
[2, 5, 6]])
For a function that returns a higher dimensional array, those dimensions
are inserted in place of the `axis` dimension.
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(np.diag, -1, b)
array([[[1, 0, 0],
[0, 2, 0],
[0, 0, 3]],
[[4, 0, 0],
[0, 5, 0],
[0, 0, 6]],
[[7, 0, 0],
[0, 8, 0],
[0, 0, 9]]])
"""
# handle negative axes
arr = asanyarray(arr)
nd = arr.ndim
axis = normalize_axis_index(axis, nd)
# arr, with the iteration axis at the end
in_dims = list(range(nd))
inarr_view = transpose(arr, in_dims[:axis] + in_dims[axis + 1 :] + [axis])
# compute indices for the iteration axes, and append a trailing ellipsis to
# prevent 0d arrays decaying to scalars, which fixes gh-8642
inds = ndindex(inarr_view.shape[:-1])
inds = (ind + (Ellipsis,) for ind in inds)
# invoke the function on the first item
try:
ind0 = next(inds)
except StopIteration as e:
raise ValueError(
"Cannot apply_along_axis when any iteration dimensions are 0"
) from None
res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))
# build a buffer for storing evaluations of func1d.
# remove the requested axis, and add the new ones on the end.
# laid out so that each write is contiguous.
# for a tuple index inds, buff[inds] = func1d(inarr_view[inds])
buff = zeros(inarr_view.shape[:-1] + res.shape, res.dtype)
# permutation of axes such that out = buff.transpose(buff_permute)
buff_dims = list(range(buff.ndim))
buff_permute = (
buff_dims[0:axis]
+ buff_dims[buff.ndim - res.ndim : buff.ndim]
+ buff_dims[axis : buff.ndim - res.ndim]
)
# matrices have a nasty __array_prepare__ and __array_wrap__
if not isinstance(res, matrix):
buff = res.__array_prepare__(buff)
# save the first result, then compute and save all remaining results
buff[ind0] = res
for ind in inds:
buff[ind] = asanyarray(func1d(inarr_view[ind], *args, **kwargs))
if not isinstance(res, matrix):
# wrap the array, to preserve subclasses
buff = res.__array_wrap__(buff)
# finally, rotate the inserted axes back to where they belong
return transpose(buff, buff_permute)
else:
# matrices have to be transposed first, because they collapse dimensions!
out_arr = transpose(buff, buff_permute)
return res.__array_wrap__(out_arr)
def quote(other_args: List[str], s_ticker: str):
parser = argparse.ArgumentParser(
add_help=False,
prog="quote",
description="Current quote for stock ticker",
)
if s_ticker:
parser.add_argument(
"-t",
"--ticker",
action="store",
dest="s_ticker",
default=s_ticker,
help="Stock ticker",
)
else:
parser.add_argument(
"-t",
"--ticker",
action="store",
dest="s_ticker",
required=True,
help="Stock ticker",
)
try:
# For the case where a user uses: 'quote BB'
if other_args:
if "-" not in other_args[0]:
other_args.insert(0, "-t")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
except SystemExit:
print("")
return
ticker = yf.Ticker(ns_parser.s_ticker)
try:
quote_df = pd.DataFrame([{
"Symbol":
ticker.info["symbol"],
"Name":
ticker.info["shortName"],
"Price":
ticker.info["regularMarketPrice"],
"Open":
ticker.info["regularMarketOpen"],
"High":
ticker.info["dayHigh"],
"Low":
ticker.info["dayLow"],
"Previous Close":
ticker.info["previousClose"],
"Volume":
ticker.info["volume"],
"52 Week High":
ticker.info["fiftyTwoWeekHigh"],
"52 Week Low":
ticker.info["fiftyTwoWeekLow"],
}])
quote_df["Change"] = quote_df["Price"] - quote_df["Previous Close"]
quote_df["Change %"] = quote_df.apply(
lambda x: "{:.2f}%".format(
(x["Change"] / x["Previous Close"]) * 100),
axis="columns",
)
for c in [
"Price",
"Open",
"High",
"Low",
"Previous Close",
"52 Week High",
"52 Week Low",
"Change",
]:
quote_df[c] = quote_df[c].apply(lambda x: f"{x:.2f}")
quote_df["Volume"] = quote_df["Volume"].apply(lambda x: f"{x:,}")
quote_df = quote_df.set_index("Symbol")
quote_data = transpose(quote_df)
print(
tabulate(
quote_data,
headers=quote_data.columns,
tablefmt="fancy_grid",
stralign="right",
))
except KeyError:
print(f"Invalid stock ticker: {ns_parser.s_ticker}")
print("")
return
def apply_along_axis(func1d, axis, arr, *args, **kwargs):
"""
Apply a function to 1-D slices along the given axis.
Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a`
is a 1-D slice of `arr` along `axis`.
Parameters
----------
func1d : function
This function should accept 1-D arrays. It is applied to 1-D
slices of `arr` along the specified axis.
axis : integer
Axis along which `arr` is sliced.
arr : ndarray
Input array.
args : any
Additional arguments to `func1d`.
kwargs : any
Additional named arguments to `func1d`.
.. versionadded:: 1.9.0
Returns
-------
apply_along_axis : ndarray
The output array. The shape of `outarr` is identical to the shape of
`arr`, except along the `axis` dimension. This axis is removed, and
replaced with new dimensions equal to the shape of the return value
of `func1d`. So if `func1d` returns a scalar `outarr` will have one
fewer dimensions than `arr`.
See Also
--------
apply_over_axes : Apply a function repeatedly over multiple axes.
Examples
--------
>>> def my_func(a):
... \"\"\"Average first and last element of a 1-D array\"\"\"
... return (a[0] + a[-1]) * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(my_func, 0, b)
array([ 4., 5., 6.])
>>> np.apply_along_axis(my_func, 1, b)
array([ 2., 5., 8.])
For a function that returns a 1D array, the number of dimensions in
`outarr` is the same as `arr`.
>>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])
>>> np.apply_along_axis(sorted, 1, b)
array([[1, 7, 8],
[3, 4, 9],
[2, 5, 6]])
For a function that returns a higher dimensional array, those dimensions
are inserted in place of the `axis` dimension.
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(np.diag, -1, b)
array([[[1, 0, 0],
[0, 2, 0],
[0, 0, 3]],
[[4, 0, 0],
[0, 5, 0],
[0, 0, 6]],
[[7, 0, 0],
[0, 8, 0],
[0, 0, 9]]])
"""
# handle negative axes
arr = asanyarray(arr)
nd = arr.ndim
axis = normalize_axis_index(axis, nd)
# arr, with the iteration axis at the end
in_dims = list(range(nd))
inarr_view = transpose(arr, in_dims[:axis] + in_dims[axis+1:] + [axis])
# compute indices for the iteration axes, and append a trailing ellipsis to
# prevent 0d arrays decaying to scalars, which fixes gh-8642
inds = ndindex(inarr_view.shape[:-1])
inds = (ind + (Ellipsis,) for ind in inds)
# invoke the function on the first item
try:
ind0 = next(inds)
except StopIteration:
raise ValueError('Cannot apply_along_axis when any iteration dimensions are 0')
res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))
# build a buffer for storing evaluations of func1d.
# remove the requested axis, and add the new ones on the end.
# laid out so that each write is contiguous.
# for a tuple index inds, buff[inds] = func1d(inarr_view[inds])
buff = zeros(inarr_view.shape[:-1] + res.shape, res.dtype)
# permutation of axes such that out = buff.transpose(buff_permute)
buff_dims = list(range(buff.ndim))
buff_permute = (
buff_dims[0 : axis] +
buff_dims[buff.ndim-res.ndim : buff.ndim] +
buff_dims[axis : buff.ndim-res.ndim]
)
# matrices have a nasty __array_prepare__ and __array_wrap__
if not isinstance(res, matrix):
buff = res.__array_prepare__(buff)
# save the first result, then compute and save all remaining results
buff[ind0] = res
for ind in inds:
buff[ind] = asanyarray(func1d(inarr_view[ind], *args, **kwargs))
if not isinstance(res, matrix):
# wrap the array, to preserve subclasses
buff = res.__array_wrap__(buff)
# finally, rotate the inserted axes back to where they belong
return transpose(buff, buff_permute)
else:
# matrices have to be transposed first, because they collapse dimensions!
out_arr = transpose(buff, buff_permute)
return res.__array_wrap__(out_arr)
def linearKernel(x, y):
return dot(x, transpose(y))
def polyKernel(x,y, gamma, coef, degree):
if gamma == 0:
gamma = .01
ret = (gamma * dot(x, transpose(y)) + coef) ** degree
ret = nan_to_num(ret)
return ret
def shuffleSkin():
skin = loadmat('data/skinorig.mat')
st = transpose(skin['skin_train'])
shuffle(st)
skin['skin_train'] = transpose(st[:2000])
savemat('data/skin.mat',skin)
import sys
fr=open('D:\eclipse_workspace\Classify\data\data.txt')
macList=['c0:38:96:25:5b:c3','e0:05:c5:ba:80:40','b0:d5:9d:46:a3:9b','42:a5:89:51:c7:dd']
X=empty((4,60),numpy.int8)
for line in fr:
parts=line.split(',')
try:
poi=macList.index(parts[2])
print('poi',poi)
if poi!='-1':
print('try parts[2]:',parts[2])
lie=int(parts[-1].strip())-1
X[poi,lie] = parts[1]
except :
pass
#print('haha',parts[2])
else:
print('no error')
print("final:",type(list),type(1),type(macList))
print(X)
w=ones((4,1))
b=1
print(transpose(w))
z=dot(transpose(w),X)+1
y1=zeros((30,1))
y2=ones((30,1))
y=row_stack((y1,y2))
print(y)
#plt.plot(list)
#plt.show()
def apply_along_axis(func1d, axis, arr, *args, **kwargs):
"""
Apply a function to 1-D slices along the given axis.
Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a`
is a 1-D slice of `arr` along `axis`.
Parameters
----------
func1d : function
This function should accept 1-D arrays. It is applied to 1-D
slices of `arr` along the specified axis.
axis : integer
Axis along which `arr` is sliced.
arr : ndarray
Input array.
args : any
Additional arguments to `func1d`.
kwargs : any
Additional named arguments to `func1d`.
.. versionadded:: 1.9.0
Returns
-------
apply_along_axis : ndarray
The output array. The shape of `outarr` is identical to the shape of
`arr`, except along the `axis` dimension. This axis is removed, and
replaced with new dimensions equal to the shape of the return value
of `func1d`. So if `func1d` returns a scalar `outarr` will have one
fewer dimensions than `arr`.
See Also
--------
apply_over_axes : Apply a function repeatedly over multiple axes.
Examples
--------
>>> def my_func(a):
... \"\"\"Average first and last element of a 1-D array\"\"\"
... return (a[0] + a[-1]) * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(my_func, 0, b)
array([ 4., 5., 6.])
>>> np.apply_along_axis(my_func, 1, b)
array([ 2., 5., 8.])
For a function that returns a 1D array, the number of dimensions in
`outarr` is the same as `arr`.
>>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])
>>> np.apply_along_axis(sorted, 1, b)
array([[1, 7, 8],
[3, 4, 9],
[2, 5, 6]])
For a function that returns a higher dimensional array, those dimensions
are inserted in place of the `axis` dimension.
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(np.diag, -1, b)
array([[[1, 0, 0],
[0, 2, 0],
[0, 0, 3]],
[[4, 0, 0],
[0, 5, 0],
[0, 0, 6]],
[[7, 0, 0],
[0, 8, 0],
[0, 0, 9]]])
"""
# handle negative axes
arr = asanyarray(arr)
nd = arr.ndim
if not (-nd <= axis < nd):
raise IndexError('axis {0} out of bounds [-{1}, {1})'.format(axis, nd))
if axis < 0:
axis += nd
# arr, with the iteration axis at the end
in_dims = list(range(nd))
inarr_view = transpose(arr, in_dims[:axis] + in_dims[axis+1:] + [axis])
# compute indices for the iteration axes
inds = ndindex(inarr_view.shape[:-1])
# invoke the function on the first item
try:
ind0 = next(inds)
except StopIteration:
raise ValueError('Cannot apply_along_axis when any iteration dimensions are 0')
res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))
# build a buffer for storing evaluations of func1d.
# remove the requested axis, and add the new ones on the end.
# laid out so that each write is contiguous.
# for a tuple index inds, buff[inds] = func1d(inarr_view[inds])
buff = zeros(inarr_view.shape[:-1] + res.shape, res.dtype)
# permutation of axes such that out = buff.transpose(buff_permute)
buff_dims = list(range(buff.ndim))
buff_permute = (
buff_dims[0 : axis] +
buff_dims[buff.ndim-res.ndim : buff.ndim] +
buff_dims[axis : buff.ndim-res.ndim]
)
# matrices have a nasty __array_prepare__ and __array_wrap__
if not isinstance(res, matrix):
buff = res.__array_prepare__(buff)
# save the first result, then compute and save all remaining results
buff[ind0] = res
for ind in inds:
buff[ind] = asanyarray(func1d(inarr_view[ind], *args, **kwargs))
if not isinstance(res, matrix):
# wrap the array, to preserve subclasses
buff = res.__array_wrap__(buff)
# finally, rotate the inserted axes back to where they belong
return transpose(buff, buff_permute)
else:
# matrices have to be transposed first, because they collapse dimensions!
out_arr = transpose(buff, buff_permute)
return res.__array_wrap__(out_arr)
def _followxSingleDirection( self,
x,
direction = Direction.FORWARD,
forward_curve = None,
last_eigenvector = None,
weights = 1.):
'''Generates a partial lpc curve dictionary from the start point, x.
Arguments
---------
x : 1-dim, length m, numpy.array of floats, start point for the algorithm when m is dimension of feature space
direction : bool, proceeds in Direction.FORWARD or Direction.BACKWARD from this point (just sets sign for first eigenvalue)
forward_curve : dictionary as returned by this function, is used to detect crossing of the curve under construction with a
previously constructed curve
last_eigenvector : 1-dim, length m, numpy.array of floats, a unit vector that defines the initial direction, relative to
which the first eigenvector is biased and initial cos_neu_neu is calculated
weights : 1-dim, length n numpy.array of observation weights (can also be used to exclude
individual observations from the computation by setting their weight to zero.),
where n is the number of feature points
'''
x0 = copy(x)
N = self.Xi.shape[0]
d = self.Xi.shape[1]
it = self._lpcParameters['it']
h = array(self._lpcParameters['h'])
t0 = self._lpcParameters['t0']
rho0 = self._lpcParameters['rho0']
save_xd = empty((it,d))
eigen_vecd = empty((it,d))
c0 = ones(it)
cos_alt_neu = ones(it)
cos_neu_neu = ones(it)
lamb = empty(it) #NOTE this is named 'lambda' in the original R code
rho = zeros(it)
high_rho_points = empty((0,d))
count_points = 0
for i in range(it):
kernel_weights = self._kernd(self.Xi, x0, c0[i]*h) * weights
mu_x = average(self.Xi, axis = 0, weights = kernel_weights)
sum_weights = sum(kernel_weights)
mean_sub = self.Xi - mu_x
cov_x = dot( dot(transpose(mean_sub), numpy.diag(kernel_weights)), mean_sub) / sum_weights
#assert (abs(cov_x.transpose() - cov_x)/abs(cov_x.transpose() + cov_x) < 1e-6).all(), 'Covariance matrix not symmetric, \n cov_x = {0}, mean_sub = {1}'.format(cov_x, mean_sub)
save_xd[i] = mu_x #save first point of the branch
count_points += 1
#calculate path length
if i==0:
lamb[0] = 0
else:
lamb[i] = lamb[i-1] + sqrt(sum((mu_x - save_xd[i-1])**2))
#calculate eigenvalues/vectors
#(sorted_eigen_cov is a list of tuples containing eigenvalue and associated eigenvector, sorted descending by eigenvalue)
eigen_cov = eigh(cov_x)
sorted_eigen_cov = zip(eigen_cov[0],map(ravel,vsplit(eigen_cov[1].transpose(),len(eigen_cov[1]))))
sorted_eigen_cov.sort(key = lambda elt: elt[0], reverse = True)
eigen_norm = sqrt(sum(sorted_eigen_cov[0][1]**2))
eigen_vecd[i] = direction * sorted_eigen_cov[0][1] / eigen_norm #Unit eigenvector corresponding to largest eigenvalue
#rho parameters
rho[i] = sorted_eigen_cov[1][0] / sorted_eigen_cov[0][0] #Ratio of two largest eigenvalues
if i != 0 and rho[i] > rho0 and rho[i-1] <= rho0:
high_rho_points = vstack((high_rho_points, x0))
#angle between successive eigenvectors
if i==0 and last_eigenvector is not None:
cos_alt_neu[i] = direction * dot(last_eigenvector, eigen_vecd[i])
if i > 0:
cos_alt_neu[i] = dot(eigen_vecd[i], eigen_vecd[i-1])
#signum flipping
if cos_alt_neu[i] < 0:
eigen_vecd[i] = -eigen_vecd[i]
cos_neu_neu[i] = -cos_alt_neu[i]
else:
cos_neu_neu[i] = cos_alt_neu[i]
#angle penalization
pen = self._lpcParameters['pen']
if pen > 0:
if i == 0 and last_eigenvector is not None:
a = abs(cos_alt_neu[i])**pen
eigen_vecd[i] = a * eigen_vecd[i] + (1-a) * last_eigenvector
if i > 0:
a = abs(cos_alt_neu[i])**pen
eigen_vecd[i] = a * eigen_vecd[i] + (1-a) * eigen_vecd[i-1]
#check curve termination criteria
if i not in (0, it-1):
#crossing
cross = self._lpcParameters['cross']
if forward_curve is None:
full_curve_points = save_xd[0:i+1]
else:
full_curve_points = vstack((forward_curve['save_xd'],save_xd[0:i+1])) #inefficient, initialize then append?
if not cross:
prox = where(ravel(cdist(full_curve_points,[mu_x])) <= mean(h))[0]
if len(prox) != max(prox) - min(prox) + 1:
break
#convergence
convergence_at = self._lpcParameters['convergence_at']
conv_ratio = abs(lamb[i] - lamb[i-1]) / (2 * (lamb[i] + lamb[i-1]))
if conv_ratio < convergence_at:
break
#boundary
boundary = self._lpcParameters['boundary']
if conv_ratio < boundary:
c0[i+1] = 0.995 * c0[i]
else:
c0[i+1] = min(1.01*c0[i], 1)
#step along in direction eigen_vecd[i]
x0 = mu_x + t0 * eigen_vecd[i]
#trim output in the case where convergence occurs before 'it' iterations
curve = { 'save_xd': save_xd[0:count_points],
'eigen_vecd': eigen_vecd[0:count_points],
'cos_neu_neu': cos_neu_neu[0:count_points],
'rho': rho[0:count_points],
'high_rho_points': high_rho_points,
'lamb': lamb[0:count_points],
'c0': c0[0:count_points]
}
return curve
def __Mstep__(self):
for k in range(0,self.K):
self.c[k,:] = 1.0/self.N * sum(self.p,axis=1)
self.mean[k,:] = sum(self.p*self.data, axis=1)/sum(self.p,axis=1)
self.covm[k,:,:] = sum(self.p*dot(self.data - self.mean[k,:],transpose(self.data - self.mean[k,:])),axis=1)/sum(self.p,axis=1)
def getP(self, sample):
p = self._multConst * exp(-(1 / 2.0) * (transpose(sample - self.mu)*self._invCov*(sample - self.mu)))
return p
def Main_Iteration_V2(I, Ok, PSF, numberOfIterations, UnblurredImage=None):
#This is the main loop for the final implementation of the RLA. It calls most other functions during its execution.
currentEstimate = Ok
newEstimate = currentEstimate
newEstimate_temp = Ok
iterator = 0
ListOfImages = []
ListOfDifferences = []
stop_Flag = 0
cv2.imshow('Original Value I ', newEstimate)
cv2.waitKey(0)
cv2.destroyAllWindows()
while iterator < numberOfIterations and stop_Flag == 0:
startLoop_Time = time.perf_counter()
if iterator > 0:
if (iterator > 50) == 0:
stopMatrix, stop_List, stop_Flag = Estimate_Image_Convergence(
newEstimate, oldEstimate, 1.01, .99, .95)
#The above two loops are placeholders for future implementation of convergence functions.
text_string = 'New Est. B4 Divide %s' % iterator
ListOfImages.append(newEstimate)
oldEstimate = newEstimate
Dividend = Divide_OriginalBlurredImage(I, newEstimate,
PSF) # I/(Estimate*PSF)
text_string = 'Dividend %s' % iterator
transpose_PSF = transpose(PSF)
Dividend_by_transpose = General_Gaussian_FilterBlur(
Dividend, transpose_PSF)
#Dividend_by_transpose = PreBuilt_Gaussian_Blur(Dividend)
text_string = 'Dividend By Transpose %s' % iterator
newEstimate = np.multiply(Dividend_by_transpose, newEstimate)
# newEstimate = np.multiply(Dividend, newEstimate)# without the transpose yields green sky
out = TurnMatrix_To_uint(newEstimate)
ChangesMatrix = np.subtract(oldEstimate, out)
ListOfDifferences.append(ChangesMatrix)
endLoop_Time = time.perf_counter()
# cv2.imshow("Difference" , ChangesMatrix)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
sumPixels = np.sum(ChangesMatrix)
numberOfpixels = ((len(ChangesMatrix) * (len(ChangesMatrix[0]) * 3)))
averagePixels = sumPixels // numberOfpixels
if averagePixels < 2:
print(averagePixels)
print(sumPixels)
print(numberOfpixels)
stop_Flag = 1
print("Stop Flag Triggered %s" % iterator)
text_string = 'New Estimate iter. %s' % iterator
newEstimate = out
print(text_string)
text_string = 'New Estimate iter. %s' % iterator
# cv2.imshow(text_string , newEstimate)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
LoopTime = endLoop_Time - startLoop_Time
loop_text = ("Loop", iterator, "time: ", LoopTime)
print(loop_text)
iterator = iterator + 1
# cv2.imshow(text_string , newEstimate)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
return newEstimate, ListOfImages, ListOfDifferences
def quote(other_args: List[str], s_ticker: str):
"""Ticker quote
Parameters
----------
other_args : List[str]
Argparse arguments
s_ticker : str
Ticker
"""
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="quote",
description="Current quote for stock ticker",
)
if s_ticker:
parser.add_argument(
"-t",
"--ticker",
action="store",
dest="s_ticker",
default=s_ticker,
help="Stock ticker",
)
else:
parser.add_argument(
"-t",
"--ticker",
action="store",
dest="s_ticker",
required="-h" not in other_args,
help="Stock ticker",
)
# Price only option.
parser.add_argument(
"-p",
"--price",
action="store_true",
dest="price_only",
default=False,
help="Price only",
)
try:
# For the case where a user uses: 'quote BB'
if other_args and "-" not in other_args[0][0]:
other_args.insert(0, "-t")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
except SystemExit:
console.print("")
return
ticker = yf.Ticker(ns_parser.s_ticker)
# If price only option, return immediate market price for ticker.
if ns_parser.price_only:
console.print(
f"Price of {ns_parser.s_ticker} {ticker.info['regularMarketPrice']} \n"
)
return
try:
quote_df = pd.DataFrame(
[
{
"Symbol": ticker.info["symbol"],
"Name": ticker.info["shortName"],
"Price": ticker.info["regularMarketPrice"],
"Open": ticker.info["regularMarketOpen"],
"High": ticker.info["dayHigh"],
"Low": ticker.info["dayLow"],
"Previous Close": ticker.info["previousClose"],
"Volume": ticker.info["volume"],
"52 Week High": ticker.info["fiftyTwoWeekHigh"],
"52 Week Low": ticker.info["fiftyTwoWeekLow"],
}
]
)
quote_df["Change"] = quote_df["Price"] - quote_df["Previous Close"]
quote_df["Change %"] = quote_df.apply(
lambda x: f'{((x["Change"] / x["Previous Close"]) * 100):.2f}%',
axis="columns",
)
for c in [
"Price",
"Open",
"High",
"Low",
"Previous Close",
"52 Week High",
"52 Week Low",
"Change",
]:
quote_df[c] = quote_df[c].apply(lambda x: f"{x:.2f}")
quote_df["Volume"] = quote_df["Volume"].apply(lambda x: f"{x:,}")
quote_df = quote_df.set_index("Symbol")
quote_data = transpose(quote_df)
print_rich_table(quote_data, title="Ticker Quote", show_index=True)
except KeyError:
logger.exception("Invalid stock ticker")
console.print(f"Invalid stock ticker: {ns_parser.s_ticker}")
console.print("")
return
