I read everything related to this but still did not understand what the problem is really. Basically I use TruncatedSVD with random_state and then print explained_variance_ratio_.sum() for it. It changes every time I run the code. Is this normal?
from sklearn.decomposition import TruncatedSVD
SVD = TruncatedSVD(n_components=40, n_iter=7, random_state=42)
XSVD = SVD.fit_transform(X)
print(SVD.explained_variance_ratio_.sum())
The problem is later I use umap and plot the result graph. And I have different graphs everytime I run the code. I do not understand if this is due to TruncatedSVD or UMAP. I use random_state=42 to stop things to change but it looks like there is no effect really.
You should probably do something wrong, because I cannot reproduce your issue with scikit-learn 0.22
In [16]: import numpy as np
...: from sklearn.decomposition import TruncatedSVD
...:
...: rng = np.random.RandomState(42)
...: X = rng.randn(10000, 100)
...: def func(X):
...: SVD = TruncatedSVD(n_components=40, n_iter=7, random_state=42)
...: XSVD = SVD.fit_transform(X)
...: print(SVD.explained_variance_ratio_.sum())
...: func(X);func(X);func(X);
0.43320350603512425
0.43320350603512425
0.43320350603512425
Related
I'm working on fitting of the experimental data. In order to fit it I use the minimization of the function of residual. Everything is quite trivial, but but this time I can't find what's wrong and why the result of fitting is so weird. The example is simplified in comparison with original problem. But anyway it gives wrong parameters even when I set used values of parameters as initial guess.
import matplotlib.pyplot as plt
import numpy as np
import csv
from scipy.optimize import curve_fit, minimize
x=np.arange(0,10,0.5)
a=0.5
b=3
ini_pars=[a, b]
def func(x, a, b):
return a*x+b
plt.plot(x, func(x,a,b))
plt.show()
def fit(pars):
A,B = pars
res = (func(x,a, b)-func(x, *pars))**2
s=sum(res)
return s
bnds=[(0.1,0.5),(1,5)]
x0=[0.1,4]
opt = minimize(fit, x0, bounds=bnds)
new_pars=[opt.x[0], opt.x[0]]
example = fit(ini_pars)
print(example)
example = fit(new_pars)
print(example)
print(new_pars)
plt.plot(x, func(x, *ini_pars))
plt.plot(x, func(x, *new_pars))
plt.show()
```[enter image description here][1]
[1]: https://i.stack.imgur.com/qc1Nu.png
It should be new_pars=[opt.x[0], opt.x[1]] instead of new_pars=[opt.x[0], opt.x[0]]. Note also that you can directly extract the values by new_pars = opt.x.
So this is my code, it's written a little messy and my result is absolutely ridiculous. I have no idea how to fix it.
Also, the seaborn library does not work on my computer in any way.
.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data=pd.read_csv('Data.csv',encoding="latin1",sep=";",engine="python")
table = data.replace(0, 0.1)
plt.plot(table["RMDM"], table["BSURF"], color="#03012d", marker=".", ls="None", markersize=3, label="")
data['RMDM'] = data['RMDM'].astype(float)
data['BSURF'] = data['BSURF'].astype(float)
fig, ax = plt.subplots()
x=data['BSURF']
y=data['RMDM']
ax.set_yscale('log')
ax.set_xscale('log')
plt.style.use('classic')
plt.xlabel('B_LC')
plt.ylabel('RM/DM')
plt.plot(x,y, 'og')
from scipy.stats import linregress
df = data.loc[(data['RMDM'] >0) & (data['BSURF'] >0)]
stats = linregress(np.log10(df["RMDM"]),np.log10(df["BSURF"]))
m = stats.slope
b = stats.intercept
r = stats.rvalue
x = np.logspace(-1, 5, base=10)
y = (m*x+b)
plt.plot(x, y, c='orange', label="fit")
plt.legend()
#m,c=np.polyfit(x,y,1)
#plt.plot(x,m*x+c)
plt.grid()
plt.show()
lmplot can be used to create a linear line through your data. you correctly used np.log for the linear regression data. keep x in terms of the log.
df['log_col1']=np.log(df['col1'])
sns.lmplot(x='log_col1','y='target', data=df, ci=None)
sns.scatterplot(y='target',x='log_col1',data=df)
plt.show()
I am using astropy to define a Tundra orbit around Earth and subsequently, I would like to extract the ECI and geodetic coordinates as the object propagates in time. I was able to get something but it does not agree with what I would expect (ECI coordinates extracted from another SW). The two orbits are not even on the same plane, which is clearly wrong.
Can anybody tell me if I am doing something obviously wrong?
The plot below shows the two results. Orange is with Astropy.
import astropy
from astropy import units as u
from poliastro.bodies import Earth
from astropy.coordinates import CartesianRepresentation
from poliastro.twobody import Orbit
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
epoch = astropy.time.Time('2020-01-01T00:00:00.000', scale='tt')
# Tundra
tundra1 = Orbit.from_classical(attractor=Earth,
a = 42164 *u.km,
ecc = 0.2684 * u.one,
inc = 63.4 * u.deg,
raan = 25 * u.deg,
argp = 270 * u.deg,
nu = 50 * u.deg,
# epoch=epoch
)
def plot_orb(orb, start_t, end_t, step_t, ax, c='k'):
orb_list = []
for t in np.arange(start_t, end_t, step_t):
single_orb = orb.propagate(t*u.min)
orb_list = orb_list + [single_orb]
xyz = orb.sample().xyz
ax.plot(*xyz,'r')
s_xyz_ar = np.zeros((len(orb_list), 3))
for i, s_orb in enumerate(orb_list):
s_xyz = s_orb.represent_as(CartesianRepresentation).xyz
s_xyz_ar[i, :] = s_xyz
ax.scatter(s_xyz_ar[:, 0], s_xyz_ar[:, 1], s_xyz_ar[:, 2], c)
return s_xyz_ar, t
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
s_xyz_ar1, t1 = plot_orb(orb=tundra1, start_t=0, end_t=1440, step_t=10, ax=ax, c='k')
When I wrote that you can do this more efficiently I was under the mistaken assumption that Orbit.propagate can be called directly on an array of time steps like:
>>> tt = np.arange(0, 1440, 10) * u.min
>>> orb = tundra1.propagate(tt)
While this "works" in that it returns a new orbit with an array of epochs, it appears Orbit is not really designed to work with an array of epochs and trying to do something like orb.represent_as just returns a value for the first epoch in the array. This would be a nice possible enhancement to poliastro.
However, the code you wrote for the scatter plot can still be significantly simplified to something like this:
>>> tt = np.arange(0, 1440, 10) * u.min
>>> xyz = np.vstack([tundra1.propagate(t).represent_as(CartesianRepresentation).xyz for t in tt])
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111, projection='3d')
>>> ax.scatter(*xyz.T)
>>> fig.show()
Result:
Ideally you should be able to do this without the np.vstack and instead just call tundra1.propagate(tt).represent_as(CartesianRepresentation).xyz without a for loop. But as the above demonstrates you can still simplify a lot by using np.vstack to make an array from a list of (x, y, z) triplets.
I'm not sure this really answers your original question though, which it seems you found the answer to that wasn't really related to the code. Still, I hope this helps!
I try to use jupyter_client to get the matplotlib output and display it in my web app. Here's what I did. But io.stdout is just a string. How can I get the actual image data of matplotlib. Thanks
In [6]: with capture_output() as io:
...: reply = kc.execute_interactive("import matplotlib.pyplot as plt\n%matplotlib inline\ndata=[1,1,2,3,4]\nplt.figure()\nplt.plot(data)", timeout=TI
...: MEOUT)
...:
...:
In [8]: io.stdout
Out[8]: '[<matplotlib.lines.Line2D at 0x1086a8d30>]<matplotlib.figure.Figure at 0x106f220f0>'
I am trying to generate an interactive plot that depends on widgets.
The problem I have is that when I change parameters using the slider, a new plot is done after the previous one, instead I would expect only one plot changing according to the parameters.
Example:
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
def plot_func(freq):
x = np.linspace(0, 2*np.pi)
y = np.sin(x * freq)
plt.plot(x, y)
interact(plot_func, freq = widgets.FloatSlider(value=7.5,
min=1,
max=5.0,
step=0.5))
After moving the slider to 4.0, I have:
while I just want one figure to change as I move the slider.
How can I achieve this?
(I am using Python 2.7, matplotlib 2.0 and I have just updated notebook and jupyter to the latest version. let me know if further info is needed.)
As you want to change the figure, instead of creating a new one, may I suggest the following way:
Use an interactive backend; %matplotlib notebook
Update the line in the plot, instead of drawing new ones.
So the code could look something like this:
%matplotlib notebook
from ipywidgets import *
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2 * np.pi)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
line, = ax.plot(x, np.sin(x))
def update(w = 1.0):
line.set_ydata(np.sin(w * x))
fig.canvas.draw_idle()
interact(update);
Alternatively you may use plt.show() as in this answer.
This is an issue (?) introduced in the last version of jupyter and/or ipywidgets. One workaround I found was to add the line plt.show() at the end of plot_func.
For completion, here is an answer that makes use of more than one slider bar and sets the default parameters as well as the interval lengths.
%matplotlib notebook
from ipywidgets import *
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-10, 10,100)
def f(x, A, B, C):
return A*x**2 + B*x + C
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
line, = ax.plot(x, f(x, A=1, B=1, C=1))
def update(A = 1, B = 0, C = 0):
line.set_ydata(f(x,A,B,C))
fig.canvas.draw_idle()
interact(update, A = (-4,4,0.1), B = (-4,4,0.1), C = (-4,4,0.1));