def make_hist(p, n, num_points):
    data = [binomial(n, p) for _ in range(num_points)]

    # use a bar chart to show the actual binomial samples
    histogram = Counter(data)
    plt.bar([x - 0.4 for x in histogram.keys()], [v / num_points for v in histogram.values()], 0.8, color="0.75")

    mu = p * n
    sigma = math.sqrt(n * p * (1 - p))

    # use a line chart to show the normal approximation
    xs = range(min(data), max(data) + 1)
    ys = [normal_cdf(i + 0.5, mu, sigma) - normal_cdf(i - 0.5, mu, sigma) for i in xs]
    plt.plot(xs, ys)
    plt.title("Binomial Distribution vs. Normal Approximation")
    plt.show()
Exemple #2
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def make_hist(p, n, num_points):

    data = [binomial(n, p) for _ in range(num_points)]

    histogram = Counter(data)
    plt.bar([x - 0.4 for x in histogram.keys()],
            [v / num_points for v in histogram.values()],
            0.8,
            color='0.75')

    mu = p * n
    sigma = math.sqrt(n * p * (1 - p))

    xs = range(min(data), max(data) + 1)
    ys = [normal_cdf(i + 0.5, mu, sigma)
          - normal_cdf(i - 0.5, mu, sigma)
          for i in xs]
    plt.plot(xs, ys)
    plt.title("Binomial Dist. vs. Normal Approximation")
    plt.show()
Exemple #3
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def make_hist(p, n, num_points):
    data = [binomial(n, p) for _ in range(num_points)]
    
    # use a bar chart to show the actual binomial samples
    histogram = Counter(data)
    plt.bar([x - 0.4 for x in histogram.keys()],
            [v / num_points for v in histogram.values()],
            0.8,
            color='0.75')
    
    mu = p * n
    sigma = math.sqrt(n * p * (1 - p))
    
    # use a line chart to show the normal approximation
    xs = range(min(data), max(data) + 1)
    ys = [normal_cdf(i + 0.5, mu, sigma) - normal_cdf(i - 0.5, mu, sigma)
          for i in xs]
    plt.plot(xs, ys)
    plt.title("Binomial Distribution vs. Normal Approximation")
    plt.show()
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def normal_probability_between(lo, hi, mu=0, sigma=1):
    return normal_cdf(hi, mu, sigma) - normal_cdf(lo, mu, sigma)
Exemple #5
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def normal_probability_above(lo, mu=0, sigma=1):
    return 1 - normal_cdf(lo, mu, sigma)
"""
Graph plot for cumulative density function for normal distribution
"""
from matplotlib import pyplot as plt
from distributions import normal_cdf

xs = [x / 10.0 for x in range(-50, 50)]
plt.plot(xs, [normal_cdf(x, sigma=1) for x in xs], '-', label='mu=0, sigma=1')
plt.plot(xs, [normal_cdf(x, sigma=2) for x in xs], '--', label='mu=0, sigma=2')
plt.plot(xs, [normal_cdf(x, sigma=0.5) for x in xs], ':', label='mu=0,sigma=0.5')
plt.plot(xs, [normal_cdf(x, mu=-1) for x in xs], '-.', label='mu=-1, sigma=1')
plt.legend(loc=4) # bottom right
plt.title("Various Normal cdfs")
plt.show()
Exemple #7
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"""
Graph plot for cumulative density function for normal distribution
"""
from matplotlib import pyplot as plt
from distributions import normal_cdf

xs = [x / 10.0 for x in range(-50, 50)]
plt.plot(xs, [normal_cdf(x, sigma=1) for x in xs], '-', label='mu=0, sigma=1')
plt.plot(xs, [normal_cdf(x, sigma=2) for x in xs], '--', label='mu=0, sigma=2')
plt.plot(xs, [normal_cdf(x, sigma=0.5) for x in xs],
         ':',
         label='mu=0,sigma=0.5')
plt.plot(xs, [normal_cdf(x, mu=-1) for x in xs], '-.', label='mu=-1, sigma=1')
plt.legend(loc=4)  # bottom right
plt.title("Various Normal cdfs")
plt.show()
def normal_probability_between(lo, hi, mu=0, sigma=1):
    return normal_cdf(hi, mu, sigma) - normal_cdf(lo, mu, sigma)
def normal_probability_above(lo, mu=0, sigma=1):
    return 1 - normal_cdf(lo, mu, sigma)