def test_mean():
assert mean([10, 20]) == 15
assert mean([100, 300]) == 200
def test_mean_zero_divison():
with pytest.raises(ZeroDivisionError):
mean([])
# sheet = book.sheet_by_index(0)
plot_x = np.zeros(sheet.nrows-1, dtype=np.float64)
plot_y = np.zeros(sheet.nrows-1, dtype=np.float64)
for row in range(sheet.nrows):
if row==0:
plt.xlabel(sheet.cell(0,1).value)
plt.ylabel(sheet.cell(0,2).value)
pass
elif row>=1:
plot_x[row-1] = float(sheet.cell(row,1).value)
plot_y[row-1] = float(sheet.cell(row,2).value)
# 特徴量の計算
print "mean(x) = " + str(mymodule.mean(plot_x))
print "mean(y) = " + str(mymodule.mean(plot_y))
print "variance(x) = " + str(mymodule.variance(plot_x))
print "variance(y) = " + str(mymodule.variance(plot_y))
print "co-variance(x,y) = " + str(mymodule.covariance(plot_x,plot_y))
print "unbased-variance(x) = " + str(mymodule.u_variance(plot_x))
print "unbased-variance(y) = " + str(mymodule.u_variance(plot_y))
print "coefficient(x,y) = " + str(mymodule.coefficient(plot_x,plot_y))
# 回帰直線の計算
liner_a = np.zeros(2, dtype=np.float64);
mymodule.simple_liner_regression(plot_x, plot_y, liner_a)
a0 = liner_a[0]
a1 = liner_a[1]
print "(a0,a1) = (" + str(a0) + ", " + str(a1) + ")"
def test_mean():
assert mean([10, 20]) == 15
assert mean([100, 300]) == 200
def test_mean_zero_divison():
with pytest.raises(ZeroDivisionError):
mean([])
def test_mean(self):
self.assertEqual(mean([10, 20]), 15)
self.assertEqual(mean([100, 300]), 200)
self.assertEqual(mean([1, 2]), 1.5)
def test_mean(self):
self.assertEqual(mean([10, 20]), 15)
self.assertEqual(mean([100, 300]), 200)
self.assertEqual(mean([1, 2]), 1.5)