def test_random_weights():
n = 10
bounds = (0., 1.)
tot = 1.0000
low = bounds[0]
high = bounds[1]
df = pd.DataFrame(index=range(1000), columns=range(n))
for i in df.index:
df.ix[i] = ffn.random_weights(n, bounds, tot)
assert df.sum(axis=1).apply(lambda x: np.round(x, 4) == tot).all()
assert df.applymap(lambda x: (x >= low and x <= high)).all().all()
n = 4
bounds = (0., 0.25)
tot = 1.0000
low = bounds[0]
high = bounds[1]
df = pd.DataFrame(index=range(1000), columns=range(n))
for i in df.index:
df.ix[i] = ffn.random_weights(n, bounds, tot)
assert df.sum(axis=1).apply(lambda x: np.round(x, 4) == tot).all()
assert df.applymap(
lambda x: (np.round(x, 2) >= low and
np.round(x, 2) <= high)).all().all()
n = 7
bounds = (0., 0.25)
tot = 0.8000
low = bounds[0]
high = bounds[1]
df = pd.DataFrame(index=range(1000), columns=range(n))
for i in df.index:
df.ix[i] = ffn.random_weights(n, bounds, tot)
assert df.sum(axis=1).apply(lambda x: np.round(x, 4) == tot).all()
assert df.applymap(
lambda x: (np.round(x, 2) >= low and
np.round(x, 2) <= high)).all().all()
n = 10
bounds = (-.25, 0.25)
tot = 0.0
low = bounds[0]
high = bounds[1]
df = pd.DataFrame(index=range(1000), columns=range(n))
for i in df.index:
df.ix[i] = ffn.random_weights(n, bounds, tot)
assert df.sum(axis=1).apply(lambda x: np.round(x, 4) == tot).all()
assert df.applymap(
lambda x: (np.round(x, 2) >= low and
np.round(x, 2) <= high)).all().all()
def test_random_weights():
n = 10
bounds = (0., 1.)
tot = 1.0000
low = bounds[0]
high = bounds[1]
df = pd.DataFrame(index=range(1000), columns=range(n))
for i in df.index:
df.loc[i] = ffn.random_weights(n, bounds, tot)
assert df.sum(axis=1).apply(lambda x: np.round(x, 4) == tot).all()
assert df.applymap(lambda x: (x >= low and x <= high)).all().all()
n = 4
bounds = (0., 0.25)
tot = 1.0000
low = bounds[0]
high = bounds[1]
df = pd.DataFrame(index=range(1000), columns=range(n))
for i in df.index:
df.loc[i] = ffn.random_weights(n, bounds, tot)
assert df.sum(axis=1).apply(lambda x: np.round(x, 4) == tot).all()
assert df.applymap(
lambda x: (np.round(x, 2) >= low and
np.round(x, 2) <= high)).all().all()
n = 7
bounds = (0., 0.25)
tot = 0.8000
low = bounds[0]
high = bounds[1]
df = pd.DataFrame(index=range(1000), columns=range(n))
for i in df.index:
df.loc[i] = ffn.random_weights(n, bounds, tot)
assert df.sum(axis=1).apply(lambda x: np.round(x, 4) == tot).all()
assert df.applymap(
lambda x: (np.round(x, 2) >= low and
np.round(x, 2) <= high)).all().all()
n = 10
bounds = (-.25, 0.25)
tot = 0.0
low = bounds[0]
high = bounds[1]
df = pd.DataFrame(index=range(1000), columns=range(n))
for i in df.index:
df.loc[i] = ffn.random_weights(n, bounds, tot)
assert df.sum(axis=1).apply(lambda x: np.round(x, 4) == tot).all()
assert df.applymap(
lambda x: (np.round(x, 2) >= low and
np.round(x, 2) <= high)).all().all()
def __call__(self, target):
sel = target.temp['selected']
n = len(sel)
w = {}
try:
rw = ffn.random_weights(n, self.bounds, self.weight_sum)
w = dict(list(zip(sel, rw)))
except ValueError:
pass
target.temp['weights'] = w
return True
def test_random_weights_throws_error():
try:
ffn.random_weights(2, (0., 0.25), 1.0)
assert False
except ValueError:
assert True
try:
ffn.random_weights(10, (0.5, 0.25), 1.0)
assert False
except ValueError:
assert True
try:
ffn.random_weights(10, (0.5, 0.75), 0.2)
assert False
except ValueError:
assert True
def test_random_weights_throws_error():
try:
ffn.random_weights(2, (0., 0.25), 1.0)
assert False
except ValueError:
assert True
try:
ffn.random_weights(10, (0.5, 0.25), 1.0)
assert False
except ValueError:
assert True
try:
ffn.random_weights(10, (0.5, 0.75), 0.2)
assert False
except ValueError:
assert True
