Python numpy.nanvar() Examples
The following are 30 code examples for showing how to use numpy.nanvar(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
| Project: recruit Author: Frank-qlu File: test_nanfunctions.py License: Apache License 2.0 | 7 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example 2
| Project: recruit Author: Frank-qlu File: test_nanfunctions.py License: Apache License 2.0 | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example 3
| Project: lambda-packs Author: ryfeus File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example 4
| Project: lambda-packs Author: ryfeus File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example 5
| Project: lambda-packs Author: ryfeus File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with suppress_warnings() as sup:
sup.record(RuntimeWarning)
sup.filter(np.ComplexWarning)
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(sup.log) == 1)
else:
assert_(len(sup.log) == 0)
Example 6
| Project: auto-alt-text-lambda-api Author: abhisuri97 File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(w) == 1)
assert_(issubclass(w[0].category, RuntimeWarning))
else:
assert_(len(w) == 0)
Example 7
| Project: vnpy_crypto Author: birforce File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example 8
| Project: vnpy_crypto Author: birforce File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example 9
| Project: vnpy_crypto Author: birforce File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with suppress_warnings() as sup:
sup.record(RuntimeWarning)
sup.filter(np.ComplexWarning)
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(sup.log) == 1)
else:
assert_(len(sup.log) == 0)
Example 10
| Project: Computable Author: ktraunmueller File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(w) == 1)
assert_(issubclass(w[0].category, RuntimeWarning))
else:
assert_(len(w) == 0)
Example 11
| Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example 12
| Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example 13
| Project: GraphicDesignPatternByPython Author: Relph1119 File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example 14
| Project: GraphicDesignPatternByPython Author: Relph1119 File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example 15
| Project: predictive-maintenance-using-machine-learning Author: awslabs File: test_nanfunctions.py License: Apache License 2.0 | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example 16
| Project: predictive-maintenance-using-machine-learning Author: awslabs File: test_nanfunctions.py License: Apache License 2.0 | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example 17
| Project: cupy Author: cupy File: meanvar.py License: MIT License | 6 votes |
|
def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
"""Returns the variance along an axis ignoring NaN values.
Args:
a (cupy.ndarray): Array to compute variance.
axis (int): Along which axis to compute variance. The flattened array
is used by default.
dtype: Data type specifier.
out (cupy.ndarray): Output array.
keepdims (bool): If ``True``, the axis is remained as an axis of
size one.
Returns:
cupy.ndarray: The variance of the input array along the axis.
.. seealso:: :func:`numpy.nanvar`
"""
if a.dtype.kind in 'biu':
return a.var(axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims)
# TODO(okuta): check type
return _statistics._nanvar(
a, axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)
Example 18
| Project: pySINDy Author: luckystarufo File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example 19
| Project: pySINDy Author: luckystarufo File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example 20
| Project: ME-Net Author: YyzHarry File: scaler.py License: MIT License | 6 votes |
|
def residual(self, X_normalized):
total = 0
if self.center_rows:
row_means = np.nanmean(X_normalized, axis=1)
total += (row_means ** 2).sum()
if self.center_columns:
column_means = np.nanmean(X_normalized, axis=0)
total += (column_means ** 2).sum()
if self.scale_rows:
row_variances = np.nanvar(X_normalized, axis=1)
row_variances[row_variances == 0] = 1.0
total += (np.log(row_variances) ** 2).sum()
if self.scale_columns:
column_variances = np.nanvar(X_normalized, axis=0)
column_variances[column_variances == 0] = 1.0
total += (np.log(column_variances) ** 2).sum()
return total
Example 21
| Project: ad_examples Author: shubhomoydas File: expressions.py License: MIT License | 6 votes |
|
def __init__(self, vals=None):
"""Initializes the mean and variance of the Gaussian variable."""
DType.__init__(self)
if vals is None:
vals = [0, 1] # some dummy. This is more for information.
# Ignore NaNs
n = np.count_nonzero(~np.isnan(vals))
if n > 0:
self.mean = np.nanmean(vals)
self.variance = np.nanvar(vals)
else:
self.mean = 0
self.variance = 0
Example 22
| Project: fancyimpute Author: iskandr File: scaler.py License: Apache License 2.0 | 6 votes |
|
def residual(self, X_normalized):
total = 0
if self.center_rows:
row_means = np.nanmean(X_normalized, axis=1)
total += (row_means ** 2).sum()
if self.center_columns:
column_means = np.nanmean(X_normalized, axis=0)
total += (column_means ** 2).sum()
if self.scale_rows:
row_variances = np.nanvar(X_normalized, axis=1)
row_variances[row_variances == 0] = 1.0
total += (np.log(row_variances) ** 2).sum()
if self.scale_columns:
column_variances = np.nanvar(X_normalized, axis=0)
column_variances[column_variances == 0] = 1.0
total += (np.log(column_variances) ** 2).sum()
return total
Example 23
| Project: mxnet-lambda Author: awslabs File: test_nanfunctions.py License: Apache License 2.0 | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example 24
| Project: mxnet-lambda Author: awslabs File: test_nanfunctions.py License: Apache License 2.0 | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example 25
| Project: mxnet-lambda Author: awslabs File: test_nanfunctions.py License: Apache License 2.0 | 6 votes |
|
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with suppress_warnings() as sup:
sup.record(RuntimeWarning)
sup.filter(np.ComplexWarning)
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(sup.log) == 1)
else:
assert_(len(sup.log) == 0)
Example 26
| Project: sureal Author: Netflix File: subjective_model_test.py License: Apache License 2.0 | 6 votes |
|
def test_observer_content_aware_subjective_model(self):
subjective_model = MaximumLikelihoodEstimationModel.from_dataset_file(
self.dataset_filepath)
result = subjective_model.run_modeling(force_subjbias_zeromean=False)
self.assertAlmostEqual(float(np.nansum(result['content_ambiguity'])), 2.653508643860357, places=4)
self.assertAlmostEqual(float(np.nanvar(result['content_ambiguity'])), 0.0092892978862108271, places=4)
self.assertAlmostEqual(float(np.sum(result['observer_bias'])), -0.020313188445860726, places=4)
self.assertAlmostEqual(float(np.var(result['observer_bias'])), 0.091830942654165318, places=4)
self.assertAlmostEqual(float(np.sum(result['observer_inconsistency'])), 11.232923468639161, places=4)
self.assertAlmostEqual(float(np.var(result['observer_inconsistency'])), 0.027721095664357907, places=4)
self.assertAlmostEqual(float(np.sum(result['quality_scores'])), 177.88599894484821, places=4)
self.assertAlmostEqual(float(np.var(result['quality_scores'])), 1.4896077857605587, places=4)
# self.assertAlmostEqual(np.nansum(result['content_ambiguity_std']), 0.30465244947706538, places=4)
self.assertAlmostEqual(float(np.sum(result['observer_bias_std'])), 2.165903882505483, places=4)
self.assertAlmostEqual(float(np.sum(result['observer_inconsistency_std'])), 27.520643824238352, places=4)
self.assertAlmostEqual(float(np.sum(result['quality_scores_std'])), 5.7355563435912256, places=4)
Example 27
| Project: ImageFusion Author: pfchai File: test_nanfunctions.py License: MIT License | 6 votes |
|
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(w) == 1)
assert_(issubclass(w[0].category, RuntimeWarning))
else:
assert_(len(w) == 0)
Example 28
| Project: dynamo-release Author: aristoteleo File: moments.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def calc_12_mom_labeling(data, t, calculate_2_mom=True):
t_uniq = np.unique(t)
m = np.zeros((data.shape[0], len(t_uniq)))
if calculate_2_mom: v =np.zeros((data.shape[0], len(t_uniq)))
for i in range(data.shape[0]):
data_ = (
np.array(data[i].A.flatten(), dtype=float)
if issparse(data)
else np.array(data[i], dtype=float)
) # consider using the `adata.obs_vector`, `adata.var_vector` methods or accessing the array directly.
m[i] = strat_mom(data_, t, np.nanmean)
if calculate_2_mom: v[i] = strat_mom(data_, t, np.nanvar)
return (m, v, t_uniq) if calculate_2_mom else (m, t_uniq)
Example 29
| Project: dynamo-release Author: aristoteleo File: moments.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def __init__(self, adata, time_key="Time", has_nan=False):
# self.data = adata
self.__dict__ = adata.__dict__
# calculate first and second moments from data
self.times = np.array(self.obs[time_key].values, dtype=float)
self.uniq_times = np.unique(self.times)
nT = self.get_n_times()
ng = self.get_n_genes()
self.M = np.zeros((ng, nT)) # first moments (data)
self.V = np.zeros((ng, nT)) # second moments (data)
for g in tqdm(range(ng), desc="calculating 1/2 moments"):
tmp = self[:, g].layers["new"]
L = (
np.array(tmp.A, dtype=float)
if issparse(tmp)
else np.array(tmp, dtype=float)
) # consider using the `adata.obs_vector`, `adata.var_vector` methods or accessing the array directly.
if has_nan:
self.M[g] = strat_mom(L, self.times, np.nanmean)
self.V[g] = strat_mom(L, self.times, np.nanvar)
else:
self.M[g] = strat_mom(L, self.times, np.mean)
self.V[g] = strat_mom(L, self.times, np.var)
Example 30
| Project: pca-magic Author: allentran File: _ppca.py License: Apache License 2.0 | 5 votes |
|
def _calc_var(self):
if self.data is None:
raise RuntimeError('Fit the data model first.')
data = self.data.T
# variance calc
var = np.nanvar(data, axis=1)
total_var = var.sum()
self.var_exp = self.eig_vals.cumsum() / total_var
