本文整理汇总了Python中sklearn.decomposition.FactorAnalysis方法的典型用法代码示例。如果您正苦于以下问题:Python decomposition.FactorAnalysis方法的具体用法?Python decomposition.FactorAnalysis怎么用?Python decomposition.FactorAnalysis使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.decomposition
的用法示例。
在下文中一共展示了decomposition.FactorAnalysis方法的8个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: dim_reduction_method
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import FactorAnalysis [as 别名]
def dim_reduction_method(self):
"""
select dimensionality reduction method
"""
if self.dim_reduction=='pca':
return PCA()
elif self.dim_reduction=='factor-analysis':
return FactorAnalysis()
elif self.dim_reduction=='fast-ica':
return FastICA()
elif self.dim_reduction=='kernel-pca':
return KernelPCA()
elif self.dim_reduction=='sparse-pca':
return SparsePCA()
elif self.dim_reduction=='truncated-svd':
return TruncatedSVD()
elif self.dim_reduction!=None:
raise ValueError('%s is not a supported dimensionality reduction method. Valid inputs are: \
"pca","factor-analysis","fast-ica,"kernel-pca","sparse-pca","truncated-svd".'
%(self.dim_reduction))
示例2: test_objectmapper
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import FactorAnalysis [as 别名]
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.decomposition.PCA, decomposition.PCA)
self.assertIs(df.decomposition.IncrementalPCA,
decomposition.IncrementalPCA)
self.assertIs(df.decomposition.KernelPCA, decomposition.KernelPCA)
self.assertIs(df.decomposition.FactorAnalysis,
decomposition.FactorAnalysis)
self.assertIs(df.decomposition.FastICA, decomposition.FastICA)
self.assertIs(df.decomposition.TruncatedSVD, decomposition.TruncatedSVD)
self.assertIs(df.decomposition.NMF, decomposition.NMF)
self.assertIs(df.decomposition.SparsePCA, decomposition.SparsePCA)
self.assertIs(df.decomposition.MiniBatchSparsePCA,
decomposition.MiniBatchSparsePCA)
self.assertIs(df.decomposition.SparseCoder, decomposition.SparseCoder)
self.assertIs(df.decomposition.DictionaryLearning,
decomposition.DictionaryLearning)
self.assertIs(df.decomposition.MiniBatchDictionaryLearning,
decomposition.MiniBatchDictionaryLearning)
self.assertIs(df.decomposition.LatentDirichletAllocation,
decomposition.LatentDirichletAllocation)
示例3: FA
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import FactorAnalysis [as 别名]
def FA(data, dim):
fa = FactorAnalysis(n_components=dim)
fa.fit(data)
return fa.transform(data)
示例4: __init__
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import FactorAnalysis [as 别名]
def __init__(self, **kwargs):
super().__init__()
self.estimator = sk_d.FactorAnalysis(**kwargs)
示例5: compute_scores
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import FactorAnalysis [as 别名]
def compute_scores(X):
pca = PCA()
fa = FactorAnalysis()
pca_scores, fa_scores = [], []
for n in n_components:
pca.n_components = n
fa.n_components = n
pca_scores.append(np.mean(cross_val_score(pca, X)))
fa_scores.append(np.mean(cross_val_score(fa, X)))
return pca_scores, fa_scores
示例6: __init__
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import FactorAnalysis [as 别名]
def __init__(self, inverse_l2=0.0001, verbose=False):
self.verbose = verbose
self.model = pipeline.make_pipeline(
decomposition.FactorAnalysis(),
LogisticRegression(
C=inverse_l2, solver='liblinear', verbose=verbose)
)
示例7: testAlgorithm
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import FactorAnalysis [as 别名]
def testAlgorithm():
import matplotlib.pyplot as plt
random.seed(35)
np.random.seed(32)
n = 200
d = 20
k = 2
sigma = .3
n_clusters = 3
decay_coef = .1
X, Y, Z, ids = generateSimulatedDimensionalityReductionData(n_clusters, n, d, k, sigma, decay_coef)
Zhat, params = block_ZIFA.fitModel(Y, k)
colors = ['red', 'blue', 'green']
cluster_ids = sorted(list(set(ids)))
model = FactorAnalysis(n_components=k)
factor_analysis_Zhat = model.fit_transform(Y)
plt.figure(figsize=[15, 5])
plt.subplot(131)
for id in cluster_ids:
plt.scatter(Z[ids == id, 0], Z[ids == id, 1], color=colors[id - 1], s=4)
plt.title('True Latent Positions\nFraction of Zeros %2.3f' % (Y == 0).mean())
plt.xlim([-4, 4])
plt.ylim([-4, 4])
plt.subplot(132)
for id in cluster_ids:
plt.scatter(Zhat[ids == id, 0], Zhat[ids == id, 1], color=colors[id - 1], s=4)
plt.xlim([-4, 4])
plt.ylim([-4, 4])
plt.title('ZIFA Estimated Latent Positions')
# title(titles[method])
plt.subplot(133)
for id in cluster_ids:
plt.scatter(factor_analysis_Zhat[ids == id, 0], factor_analysis_Zhat[ids == id, 1], color = colors[id - 1], s = 4)
plt.xlim([-4, 4])
plt.ylim([-4, 4])
plt.title('Factor Analysis Estimated Latent Positions')
plt.show()
示例8: initializeParams
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import FactorAnalysis [as 别名]
def initializeParams(Y, K, singleSigma=False, makePlot=False):
"""
initializes parameters using a standard factor analysis model (on imputed data) + exponential curve fitting.
Checked.
Input:
Y: data matrix, n_samples x n_genes
K: number of latent components
singleSigma: uses only a single sigma as opposed to a different sigma for every gene
makePlot: makes a mu - p_0 plot and shows the decaying exponential fit.
Returns:
A, mus, sigmas, decay_coef: initialized model parameters.
"""
N, D = Y.shape
model = FactorAnalysis(n_components=K)
zeroedY = deepcopy(Y)
mus = np.zeros([D, 1])
for j in range(D):
non_zero_idxs = np.abs(Y[:, j]) > 1e-6
mus[j] = zeroedY[:, j].mean()
zeroedY[:, j] = zeroedY[:, j] - mus[j]
model.fit(zeroedY)
A = model.components_.transpose()
sigmas = np.atleast_2d(np.sqrt(model.noise_variance_)).transpose()
if singleSigma:
sigmas = np.mean(sigmas) * np.ones(sigmas.shape)
# Now fit decay coefficient
means = []
ps = []
for j in range(D):
non_zero_idxs = np.abs(Y[:, j]) > 1e-6
means.append(Y[non_zero_idxs, j].mean())
ps.append(1 - non_zero_idxs.mean())
decay_coef, pcov = curve_fit(exp_decay, means, ps, p0=.05)
decay_coef = decay_coef[0]
mse = np.mean(np.abs(ps - np.exp(-decay_coef * (np.array(means) ** 2))))
if (mse > 0) and makePlot:
from matplotlib.pyplot import figure, scatter, plot, title, show
figure()
scatter(means, ps)
plot(np.arange(min(means), max(means), .1), np.exp(-decay_coef * (np.arange(min(means), max(means), .1) ** 2)))
title('Decay Coef is %2.3f; MSE is %2.3f' % (decay_coef, mse))
show()
return A, mus, sigmas, decay_coef