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import numpy as np | |
from scipy.stats import t | |
import statsmodels.api as sm | |
np.random.seed(2022) | |
# Prepare data | |
N = 51 | |
k = 1 | |
x = np.random.normal(size=N) |
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import matplotlib.pyplot as plt | |
import numpy as np | |
from scipy.stats import binom | |
n, p = 100, 0.5 | |
x = np.arange(n) | |
fig, ax = plt.subplots(1, 1) | |
ax.plot(x, binom.pmf(x, n, p), 'bo', ms=1, label='binom pmf') | |
ax.vlines(x, 0, binom.pmf(x, n, p), colors='b', lw=1, alpha=0.5) |
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from scipy.stats import beta | |
import matplotlib.pyplot as plt | |
import numpy as np | |
a, b = 0.99, 0.5 | |
fig, ax = plt.subplots(1, 1) | |
x = np.linspace(0, 1, 10000) | |
y = beta.pdf(x, a, b) |
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from scipy.stats import beta | |
import matplotlib.pyplot as plt | |
import numpy as np | |
params = [[81, 219], | |
[82, 219], | |
[81+100, 219+200]] | |
for a, b in params: |
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from scipy.stats import beta | |
import matplotlib.pyplot as plt | |
import numpy as np | |
params = (((8,2), (5,5), (2,8)), ((2,1), (1,1), (1,2)), ((0.8,0.2), (0.5,0.5), (0.2,0.8))) | |
title_suffixes = ('Bell-shape', 'Straight lines', 'U-shape') | |
colors = ('blue', 'yellow', 'red') | |
x = np.linspace(0, 1, 10000) |
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import matplotlib.pyplot as plt | |
import numpy as np | |
from mpmath import * | |
mp.pretty = True | |
nseq = range(1, 10001) | |
hseq = [] | |
for i in nseq: | |
mp.dps = i | |
x, fx = np.unique(list(str(pi).replace('.', '')), return_counts=True) |
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import matplotlib.gridspec as gridspec | |
import matplotlib.pyplot as plt | |
import numpy as np | |
gs = gridspec.GridSpec(4, 4) | |
x = np.random.randint(1,7,size=10**5) | |
n, c = np.unique(x, return_counts=True) | |
ax1 = plt.subplot(gs[:2, :2]) | |
ax1.bar(n, c) |
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import os.path as path | |
from tempfile import mkdtemp | |
import numpy as np | |
m = 10**6 | |
n = 10**3 | |
filename = path.join(mkdtemp(), 'X.dat') | |
X = np.memmap(filename, dtype='float32', mode='w+', shape=(m, n)) |
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import matplotlib.pyplot as plt | |
import numpy as np | |
import pandas as pd | |
from scipy.stats import shapiro, probplot | |
import statsmodels.api as sm | |
data = sm.datasets.scotland.load() | |
data.exog = sm.add_constant(data.exog) | |
links = [sm.families.Gaussian(), sm.families.Gamma()] |
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import numpy as np | |
n = 10 | |
m = 50 | |
s = 100 | |
y = np.mean(np.random.normal(m, s, n * 10**4).reshape(10**4, n), axis=1) | |
print(np.std(y, ddof=1), s / np.sqrt(n)) |