Python numpy.log2函数代码示例

def Classification(theta, tag_dict, feature_dict, features):
    """
    Predict the tag with the value of theta for naive bayes model

    theta value is used to predict the tag
    """
    total = sum(tag_dict.values())
    prob = []
    for tag in range(2):
        tag = str(tag)
        probability = np.log2(tag_dict[tag] / total)
        searchFlag = 1
        for featureID in range(len(features)):
            feature = features[featureID]
            item = (tag, feature, featureID)
            if item not in feature_dict:
                searchFlag = 0
                break
            tmpProb = np.log2(feature_dict[item] / tag_dict[tag])
            probability = probability + tmpProb
        prob.append(probability)
        if searchFlag == 0:
            break
    if searchFlag == 0:
        if tag == '1':
            predict_tag = '0'
        else:
            predict_tag = '1'
    else:
        ratio = 2.0 ** (prob[1] - prob[0])
        if ratio >= theta:
            predict_tag = '1'
        else:
            predict_tag = '0'
    return predict_tag

def mutual_info(X, Y):
    """ Calculate the mutual information between X and Y.

        Note: The input X, Y may look like this:

            X = [event1, event2, event3, ..., eventn]
            Y = [event1, event2, event3, ..., eventn]

        Every event_i represents the outcome of that random variable.

        Args:
            X: An array-like random variable.
            Y: An array-like random variable.

        Returns:
            The mutual information score between X and Y.
    """
    X_np = np.array(X)
    Y_np = np.array(Y)

    P_t1 = np.average(X_np)
    P_t0 = 1 - P_t1
    P_c1 = np.average(Y_np)
    P_c0 = 1 - P_c1
    P_t1c1 = np.average(X_np & Y_np)
    P_t1c0 = np.average(X_np & ~Y_np)
    P_t0c1 = np.average(~X_np & Y_np)
    P_t0c0 = np.average(~X_np & ~Y_np)

    mi = P_t1c1 * np.log2(P_t1c1 / (P_t1 * P_c1)) +\
        P_t1c0 * np.log2(P_t1c0 / (P_t1 * P_c0)) +\
        P_t0c1 * np.log2(P_t0c1 / (P_t0 * P_c1)) +\
        P_t0c0 * np.log2(P_t0c0 / (P_t0 * P_c0))

    return mi if not np.isnan(mi) else 0

def __cqt_filter_fft(sr, fmin, n_bins, bins_per_octave, tuning,
                     filter_scale, norm, sparsity, hop_length=None,
                     window='hann'):
    '''Generate the frequency domain constant-Q filter basis.'''

    basis, lengths = filters.constant_q(sr,
                                        fmin=fmin,
                                        n_bins=n_bins,
                                        bins_per_octave=bins_per_octave,
                                        tuning=tuning,
                                        filter_scale=filter_scale,
                                        norm=norm,
                                        pad_fft=True,
                                        window=window)

    # Filters are padded up to the nearest integral power of 2
    n_fft = basis.shape[1]

    if (hop_length is not None and
            n_fft < 2.0**(1 + np.ceil(np.log2(hop_length)))):

        n_fft = int(2.0 ** (1 + np.ceil(np.log2(hop_length))))

    # re-normalize bases with respect to the FFT window length
    basis *= lengths[:, np.newaxis] / float(n_fft)

    # FFT and retain only the non-negative frequencies
    fft_basis = fft.fft(basis, n=n_fft, axis=1)[:, :(n_fft // 2)+1]

    # sparsify the basis
    fft_basis = util.sparsify_rows(fft_basis, quantile=sparsity)

    return fft_basis, n_fft, lengths

 def getBits(self,cell):
         zero=[-self.markerArea[i]/2. for i in [0,1]]
         bitx=[int(i) for i in bin(int(cell[0]))[::-1][:-2]]
         bity=[int(i) for i in bin(int(cell[1]))[::-1][:-2]]
         s0=int(np.log2(self.cellsPerBlock[0]*self.noBlocks[0]))
         s1=int(np.log2(self.cellsPerBlock[1]*self.noBlocks[1]))
         for i in range(s0-len(bitx)):
             bitx.append(0)
         for i in range(s1-len(bity)):
             bity.append(0)
         tx=np.zeros(s0,dtype=np.bool)
         ty=np.zeros(s1,dtype=np.bool)
         px=np.empty((s0,2))
         py=np.empty((s1,2))
         for i,b in enumerate(bitx):
             x=zero[0]+mod(i+1,self.noBitsX)*self.bitDistance
             y=zero[1]+((i+1)/self.noBitsY)*self.bitDistance
             px[i]=(x,y)
             tx[i]=b
         for i,b in enumerate(bity):
             x=zero[0]+(self.noBitsX-mod(i+1,self.noBitsX)-1)*self.bitDistance
             y=zero[1]+(self.noBitsY-(i+1)/self.noBitsY-1)*self.bitDistance
             py[i]=(x,y)
             ty[i]=b
         return px,py,tx,ty

def hurstexp_welchper(data, samp=1.05, f_max=0, consider_fBm=False):
    """
    These functions compute the Hurst exponent of a signal using the
    Welch periodogram
    data : your signal
    samp : sampling rate in Hz 1 for an fMRI series
    f_max: the higher frequency you want to take into account
    """
    #data could be two dimensional(but no more...) in that cas time should
    #be on second position
    time_series = TimeSeries(data=data, sampling_rate=samp)
    spectral_analysis = SpectralAnalyzer(time_series)
    frq, pwr = spectral_analysis.psd
    #We need to take only the small frequency, but the exact choice is a
    #bit arbitrary we need to have alpha between 0 and 1
    if f_max==0:
        masker = frq > 0
    else:
        masker = np.all([(frq > 0), (frq < f_max)], axis=0)
    log2frq = np.log2(frq[masker])
    log2pwr = np.log2(pwr.T[masker])
    tmp = np.polyfit(log2frq, log2pwr, deg=1)
    if consider_fBm:
        return (1 - tmp[0]) / 4, {'aest': tmp[1], 'log2frq': log2frq, 'log2pwr': log2pwr}
    return (1 - tmp[0]) / 2, {'aest': tmp[1], 'log2frq': log2frq, 'log2pwr': log2pwr}

def prepare_logged(x, y):
    """
    Transform `x` and `y` to a log scale while dealing with zeros.

    This function scales `x` and `y` such that the points that are zero in one
    array are set to the min of the other array.

    When plotting expression data, frequently one sample will have reads in
    a particular feature but the other sample will not.  Expression data also
    tends to look better on a log scale, but log(0) is undefined and therefore
    cannot be shown on a plot.  This function allows these points to be shown,
    piled up along one side of the plot.

    :param x,y: NumPy arrays
    """
    xi = np.log2(x)
    yi = np.log2(y)

    xv = np.isfinite(xi)
    yv = np.isfinite(yi)

    global_min = min(xi[xv].min(), yi[yv].min())
    global_max = max(xi[xv].max(), yi[yv].max())

    xi[~xv] = global_min
    yi[~yv] = global_min

    return xi, yi

def rms_total(x, window_size=256):
	n_windows = int(pow(2,numpy.log2(len(x))-numpy.log2(window_size)))
	rms_tot = numpy.zeros(n_windows)
	for i in range(n_windows):
		w = x[i*window_size:(i+1)*window_size]
		rms_tot[i] = rms(w,window_size)
	return rms_tot

def DFA(indata,scale,q,m):
    y = np.cumsum(indata-indata.mean())             #Equation 1 in paper
    RMSt = []                                       #Temporary RMS variable: contain F(s,v) value
    F = []                                          #F: Fluctuation function
    N = len(indata)
    print 'len indata: ',N
    for i in range(len(scale)):
        ns = int(np.floor(len(y)/scale[i]))         #number of segments: Ns = int(N/s)
        for v in range(2*ns):
            if v < ns:
                index_start = v*scale[i]
                index_end = (v+1)*scale[i]
            else:
                index_start = N - (v-ns)*scale[i]-scale[i]
                index_end = N - (v-ns)*scale[i]
            index = range(index_start,index_end)    #calculate index for each segment
            yv = y[index_start:index_end]           #Extract values of time series for each segments
            c = np.polyfit(index,yv,m)
            fit = np.polyval(c,index)
            RMSt.append(math.sqrt(np.mean((yv-fit)**2))) #Equation 2. But calculating only F(v,s) not F(v,s)**2
        RMS = np.asarray(RMSt)                      #Convert RMSt to array
        qRMS = RMS**q
        F.append(np.mean(qRMS)**(1.0/q))              #Equation 4
        del RMSt[:]                                 #Reset RMSt[:]
    C = np.polyfit(np.log2(scale),np.log2(F),1)
    H = C[0]                                        #Hurst parameter
    return (H,scale,F)

def _hist_bin_doane(x):
    """
    Doane's histogram bin estimator.

    Improved version of Sturges' formula which works better for
    non-normal data. See
    stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning

    Parameters
    ----------
    x : array_like
        Input data that is to be histogrammed, trimmed to range. May not
        be empty.

    Returns
    -------
    h : An estimate of the optimal bin width for the given data.
    """
    if x.size > 2:
        sg1 = np.sqrt(6.0 * (x.size - 2) / ((x.size + 1.0) * (x.size + 3)))
        sigma = np.std(x)
        if sigma > 0.0:
            # These three operations add up to
            # g1 = np.mean(((x - np.mean(x)) / sigma)**3)
            # but use only one temp array instead of three
            temp = x - np.mean(x)
            np.true_divide(temp, sigma, temp)
            np.power(temp, 3, temp)
            g1 = np.mean(temp)
            return x.ptp() / (1.0 + np.log2(x.size) +
                                    np.log2(1.0 + np.absolute(g1) / sg1))
    return 0.0

	def CostFunction(self, X, y, regularization_parameter, count_sigmas = False):
		X = np.matrix(X)
		y = np.matrix(y)
		outs = self.FeedForward(X)

		J = 0

		for thetas in self.Thetas:
			J = J + np.sum(np.square(thetas[:, 1]))
		J = J * regularization_parameter
		#linear
		#J = J + np.sum(np.square(outs - y))
		#logistic
		J = J + np.sum(- (np.multiply(y, np.log2(outs)) + np.multiply((1 - y), np.log2(1 - outs))))
		J = J / len(X)
		
		if count_sigmas == False:
			return J

		#linear
		#self.Sigmas[-1] = np.multiply(np.multiply((outs - y), outs), 1 - outs) * 2
		#logistic
		self.Sigmas[-1] = (outs - y) / math.log1p(1)

		for i in reversed(range(0, self.n_hidden_layers)):
			self.Sigmas[i] = np.multiply(self.activations[i + 1][:, 1:], 1 - self.activations[i + 1][:, 1:])
			self.Sigmas[i] = np.multiply(self.Sigmas[i], self.Sigmas[i + 1] * self.Thetas[i + 1][:, 1:]) 

		return J

def center_zoom(Lngs, Lats):
	# Find the bounding box
	minLon, minLat, maxLon, maxLat = min(Lngs), min(Lats), max(Lngs), max(Lats)
	deltaLon, deltaLat = (maxLon - minLon),  (maxLat - minLat)

	centerLon = minLon + .5*deltaLon
	centerLat = minLat + .5*deltaLat

	zoomxfac = 3600.
	zoomyfac = 2925.
	if deltaLon != 0:
		pixXperdeg =  (512.0/deltaLon)
	else:
		pixXperdeg = 1.
	if deltaLat != 0:
		pixYperdeg =  (512.0/deltaLat)
	else:
		pixYperdeg = 1.

	# conversion to zoom
	dx = pixXperdeg/zoomyfac
	dy = pixYperdeg/zoomyfac
	zx = np.floor(12+np.log2(dx))
	zy = np.floor(12+np.log2(dy))
	zoom = min(zx, zy)
	if zoom < 10:
		zoom = 10
	if zoom > 19:
		zoom = 19
	return centerLon, centerLat, zoom

def smartirs_wglobal(docfreq, totaldocs, global_scheme):
    """Calculate global document weight based on the weighting scheme specified in `global_scheme`.

    Parameters
    ----------
    docfreq : int
        Document frequency.
    totaldocs : int
        Total number of documents.
    global_scheme : {'n', 't', 'p'}
        Global transformation scheme.

    Returns
    -------
    float
        Calculated global weight.

    """

    if global_scheme == "n":
        return 1.
    elif global_scheme == "t":
        return np.log2(1.0 * totaldocs / docfreq)
    elif global_scheme == "p":
        return max(0, np.log2((1.0 * totaldocs - docfreq) / docfreq))

def smartirs_wlocal(tf, local_scheme):
    """Calculate local term weight for a term using the weighting scheme specified in `local_scheme`.

    Parameters
    ----------
    tf : int
        Term frequency.
    local : {'n', 'l', 'a', 'b', 'L'}
        Local transformation scheme.

    Returns
    -------
    float
        Calculated local weight.

    """
    if local_scheme == "n":
        return tf
    elif local_scheme == "l":
        return 1 + np.log2(tf)
    elif local_scheme == "a":
        return 0.5 + (0.5 * tf / tf.max(axis=0))
    elif local_scheme == "b":
        return tf.astype('bool').astype('int')
    elif local_scheme == "L":
        return (1 + np.log2(tf)) / (1 + np.log2(tf.mean(axis=0)))

def plot_profiles(prots, eluts, sp='Hs', plot_sums=True, shape=None,
        min_count=1):
    """
    shape: (m,n) = m rows, n columns
    eluts: [el.NormElut(f, sp, norm_rows=False, norm_cols=False) for f in
    fs]
    """
    import plotting as pl
    gt = seqs.GTrans()
    use_eluts = elutions_containing_prots(eluts, sp, seqs.names2ids(prots),
            min_count)
    shape = shape if shape else ut.sqrt_shape(len(use_eluts)+1)
    fig = pl.figure()
    for i,e in enumerate(use_eluts):
        sp_target = ut.shortname(e.filename)[:2]
        pl.subplot(shape[0],shape[1],i+1)
        pl.title(ut.shortname(e.filename))
        pids = [gt.name2id[p] for p in prots]
        protsmax = max([np.max(e.normarr[r]) for p in pids if p in e.baseid2inds for
            r in e.baseid2inds[p]])
        plot_prots(e, pids, e.baseid2inds, protsmax)
        if plot_sums:
            # plot total spectral counts normalized to match biggest peak
            sums = np.sum(e.normarr,axis=0)
            fmax = np.max(sums)
            pl.plot(range(sums.shape[1]),
                    np.log2(sums[0,:]).T*np.log2(protsmax)*len(pids)/np.log2(fmax), 
                    color='k', linestyle='-', linewidth=.5)
    # make legend with all prots
    pl.subplot(shape[0],shape[1],0)
    for p in prots: pl.plot(0,label=p)
    pl.legend()

def means2idxarrays(g1, g2, i_bins, c_bins, difference):
    '''take two arrays of values and return the initial values
    and differences as numpy digitised arrays'''

    if difference == "relative":
        # calculate difference between mean values for group1 and group2
        # g1 and g2 always the same length
        change = [g2[x] - g1[x] for x in range(0, len(g1))]
        initial = g1

    elif difference == "logfold":
        change = [np.log2((g2[x] + 1.0) / (g1[x] + 1.0))
                  for x in range(0, len(g1))]
        initial = [np.log2(g1[x] + 1.0) for x in range(0, len(g1))]

    elif difference == "abs_logfold":
        change = [abs(np.log2((g2[x] + 1.0) / (g1[x] + 1.0)))
                  for x in range(0, len(g1))]
        initial = [max(np.log2(g1[x] + 1.0), np.log2(g2[x] + 1.0))
                   for x in range(0, len(g1))]

    # return arrays of len(change) with the index position in c_bins
    # corresponding to the bin in which the value of change falls
    change_idx = np.digitize(change, c_bins, right=True)
    initial_idx = np.digitize(initial, i_bins, right=True)

    return(change_idx, initial_idx)