zaltoprofen

#from PIL import Image, ImageDraw
from svgwrite import Drawing, rgb
from cmath import tan, pi
import hashlib

a = hashlib.sha256(input('key: ').encode('utf-8'))
digest = a.digest()
#img = Image.new('RGBA', (256, 256), (255, 255, 255, 255))
#draw = ImageDraw.Draw(img, 'RGBA')
draw = Drawing('icon.svg', size=(256, 256))

zaltoprofen

#include <iostream>
#include <vector>
#include <map>
#include <set>
#include <unordered_map>
#include <unordered_set>
#include <algorithm>
#include <limits>
#include <functional>
#include <queue>

zaltoprofen

package main

import (
	"log"
	"net/url"
	"os"
	"time"

	"github.com/ChimeraCoder/anaconda"
	"github.com/joho/godotenv"

zaltoprofen

package main

import (
	"fmt"
	"log"
	"net/url"
	"os"

	"github.com/ChimeraCoder/anaconda"
	"github.com/deckarep/golang-set"

zaltoprofen

WHY??? Warum nicht???

tried to access class twitter4j.StreamListener from class StreamTask
java.lang.IllegalAccessError: tried to access class twitter4j.StreamListener from class StreamTask
	at StreamTask.run(StreamTask.kt:34)
	at com.intellij.openapi.progress.impl.CoreProgressManager$TaskRunnable.run(CoreProgressManager.java:563)
	at com.intellij.openapi.progress.impl.CoreProgressManager$2.run(CoreProgressManager.java:142)
	at com.intellij.openapi.progress.impl.CoreProgressManager.registerIndicatorAndRun(CoreProgressManager.java:446)
	at com.intellij.openapi.progress.impl.CoreProgressManager.executeProcessUnderProgress(CoreProgressManager.java:392)
	at com.intellij.openapi.progress.impl.ProgressManagerImpl.executeProcessUnderProgress(ProgressManagerImpl.java:54)

zaltoprofen

import com.intellij.openapi.project.Project
import com.intellij.openapi.wm.ToolWindow
import com.intellij.openapi.wm.ToolWindowFactory
import java.awt.*
import javax.swing.JComponent
import javax.swing.JLabel
import javax.swing.JPanel
import javax.swing.JTextField

class HogeWindow: ToolWindowFactory {

zaltoprofen

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression, Lasso
from sklearn.pipeline import Pipeline
import numpy as np
import matplotlib.pyplot as plt

modelF = lambda deg: Pipeline([
    ('poly', PolynomialFeatures(degree=deg)),
    ('linear', LinearRegression(fit_intercept=False))])

zaltoprofen

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression, Lasso
from sklearn.pipeline import Pipeline
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats

modelF = lambda deg: Pipeline([
    ('poly', PolynomialFeatures(degree=deg)),
    ('linear', LinearRegression(fit_intercept=False))])

zaltoprofen

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression, Lasso, Ridge
from sklearn.pipeline import Pipeline
import numpy as np
import matplotlib.pyplot as plt

def f(x):
    return 2 + 3*x - x**2

X = (np.random.rand(80) * 10 - 5)

zaltoprofen

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression, Lasso, Ridge
from sklearn.pipeline import Pipeline
import numpy as np
import matplotlib.pyplot as plt

def f(x):
    return 2 + 3*x - x**2

X = (np.random.rand(50) * 10 - 5)