python类std()的实例源码

def k_means_cluster_Predict(data_list,info):
    array_diagnal=np.array([[data_list[0][x],data_list[1][x]] for x in range(len(data_list[0]))])
    ks = list(range(1,len(info)))
    KMeans = [cluster.KMeans(n_clusters = i, init="k-means++").fit(array_diagnal) for i in ks]
    BIC = [compute_bic(kmeansi,array_diagnal) for kmeansi in KMeans]
    ks_picked=ks[BIC.index(max(BIC))]
    if ks_picked==1:
        return [data_list]
    else:
        out=[]
        std_rec=[scipy.std(data_list[0]),scipy.std(data_list[1])]
        whitened = whiten(array_diagnal)
        centroids, distortion=kmeans(whitened,ks_picked)
        idx,_= vq(whitened,centroids)
        for x in range(ks_picked):
            group1=[[int(i) for i in array_diagnal[idx==x,0]],[int(i) for i in array_diagnal[idx==x,1]]]
            out.append(group1)
        return out

def getForegroundMask(self):
        '''
        @return: A mask image indicating which pixels are considered foreground.
          Depending on whether soft-thresholding is used, this may be a binary image
          with values of [0 or 255], or image of weights [0.0-255.0], which will
          have to be divided by 255 to get weights [0.0-1.0].         
        @note: One may wish to perform additional morphological operations
            on the foreground mask prior to use.
        '''
        diff = self._computeBGDiff()
        if self._softThreshold:
            mask = 1 - (math.e)**(-(1.0*diff)/self._threshold)  #element-wise exp weighting
            #mask = (diff > self._threshold)   
        else:
            mask = (sp.absolute(diff) > self._threshold)    
            #mu = sp.mean(diff)
            #sigma = sp.std(diff)
            #mask = sp.absolute((diff-mu)/sigma) > self._threshold
        return pv.Image(mask*255.0)

def get_html(self, base_file_name: str, h_level: int) -> str:
        sp = None # type: SingleProperty
        columns = [
            BOTableColumn("n", "{:5d}", lambda sp, _: sp.observations(), first),
            BOTableColumn("mean", "{:10.5f}", lambda sp, _: sp.mean(), first),
            BOTableColumn("mean / best mean", "{:5.5%}", lambda sp, means: sp.mean() / min(means), first),
            BOTableColumn("mean / mean of first impl", "{:5.5%}", lambda sp, means: sp.mean() / means[0], first),
            BOTableColumn("std / mean", "{:5.5%}", lambda sp, _: sp.std_dev_per_mean(), first),
            BOTableColumn("std / best mean", "{:5.5%}", lambda sp, means: sp.std_dev() / min(means), first),
            BOTableColumn("std / mean of first impl", "{:5.5%}", lambda sp, means: sp.std_dev() / means[0], first),
            BOTableColumn("median", "{:5.5f}", lambda sp, _: sp.median(), first)
        ]
        html = """
        <h{h}>Input: {input}</h{h}>
        The following plot shows the actual distribution of the measurements for each implementation.
        {box_plot}
        """.format(h=h_level, input=repr(self.input), box_plot=self.get_box_plot_html(base_file_name))
        html += self.table_html_for_vals_per_impl(columns, base_file_name)
        return html

def k_means_cluster(data_list):
    if max(data_list[0])-min(data_list[0])>10 and max(data_list[1])-min(data_list[1])>10:
        array_diagnal=np.array([[data_list[0][x],data_list[1][x]] for x in range(len(data_list[0]))])
        ks = list(range(1,min([5,len(data_list[0])+1])))
        KMeans = [cluster.KMeans(n_clusters = i, init="k-means++").fit(array_diagnal) for i in ks]
        KMeans_predict=[cluster.KMeans(n_clusters = i, init="k-means++").fit_predict(array_diagnal) for i in ks]
        BIC=[]
        BIC_rec=[]
        for x in ks:
            if KMeans_predict[x-1].max()<x-1: continue
            else:
                BIC_i=compute_bic(KMeans[x-1],array_diagnal)
                if abs(BIC_i)<10**8:
                    BIC.append(BIC_i)
                    BIC_rec.append(x)
        #BIC = [compute_bic(kmeansi,array_diagnal) for kmeansi in KMeans]
        #ks_picked=ks[BIC.index(max(BIC))]
        ks_picked=BIC_rec[BIC.index(max(BIC))]
        if ks_picked==1:
            return [data_list]
        else:
            out=[]
            std_rec=[scipy.std(data_list[0]),scipy.std(data_list[1])]
            whitened = whiten(array_diagnal)
            centroids, distortion=kmeans(whitened,ks_picked)
            idx,_= vq(whitened,centroids)
            for x in range(ks_picked):
                group1=[[int(i) for i in array_diagnal[idx==x,0]],[int(i) for i in array_diagnal[idx==x,1]]]
                out.append(group1)
            return out
    else:
        return [data_list]

def sharpeRatio(ticker,begdate=(2012,1,1),enddate=(2016,12,31)):
    """Objective: estimate Sharpe ratio for stock
        ticker  : stock symbol 
        begdate : beginning date
        enddate : ending date

       Example #1: sharpeRatio("ibm")
                     0.0068655583807256159

       Example #2: date1=(1990,1,1)
                   date2=(2015,12,23)
                   sharpeRatio("ibm",date1,date2)
                     0.027831010497755326
    """
    import scipy as sp
    from matplotlib.finance import quotes_historical_yahoo_ochl as getData
    p = getData(ticker,begdate, enddate,asobject=True,adjusted=True)
    ret=p.aclose[1:]/p.aclose[:-1]-1
    return sp.mean(ret)/sp.std(ret)

def amean_std(values: t.List[float]) -> float:
    """
    Calculates the arithmetic mean.
    """
    return sp.std(values)

def get_html(self, base_file_name: str, h_level: int) -> str:
        html = """
            <h{}>Program: {!r} ({} lines, {} entropy)</h{}>
            The following plot shows the mean score per input distribution for every implementation.
        """.format(h_level, self.name, self.line_number, self.entropy, h_level)
        html += self.get_box_plot_html(base_file_name)
        scores = self.get_impl_mean_scores()
        std_devs = self.get_statistical_property_scores(rel_std_dev_func)
        html += """
            <table class="table">
                <tr><th>implementation</th><th>geom mean over means relative to best (per input) aka mean score</th>
                    <th>... std dev rel. to the best mean</th>
                </tr>
        """
        for impl in scores.keys():
            html += """
                <tr><td>{}</td><td>{:5.2%}</td><td>{:5.2%}</td></tr>
            """.format(impl, stats.gmean(scores[impl]), stats.gmean(std_devs[impl]))
        html += "</table>"
        impl_names = list(scores.keys())
        for (i, input) in enumerate(self.prog_inputs.keys()):
            app = html_escape_property(input)
            if len(app) > 20:
                app = str(i)
            html += self.prog_inputs[input].get_html(base_file_name + "_" + app, h_level + 1)
        return html

def std_f(ticker):
    x=ret_monthly(ticker)
    return sp.std(x)

def portfolio_var(R,w):
    cor=corr # here!!!!
    std_dev=sp.std(R,axis=0)
    var = 0.0
    for i in xrange(n):
        for j in xrange(n):
            var += w[i]*w[j]*std_dev[i]*std_dev[j]*cor[i, j]
    return var

def portfolio_var(R,w):
    cor = sp.corrcoef(R.T)
    std_dev=sp.std(R,axis=0)
    var = 0.0
    for i in xrange(n):
        for j in xrange(n):
            var += w[i]*w[j]*std_dev[i]*std_dev[j]*cor[i, j]
    return var
# function 3: estimate Sharpe ratio

def outlier_removed_fit(m, w = None, n_iter=10, polyord=7):
    """
    Remove outliers using fited data.

    Args:
        m (:obj:`numpy array`): Phase curve.
        n_iter (:obj:'int'): Number of iteration outlier removal
        polyorder (:obj:'int'): Order of polynomial used.

    Returns:
        fit (:obj:'numpy array'): Curve with outliers removed
    """
    if w is None:
        w = sp.ones_like(m)
    W = sp.diag(sp.sqrt(w))
    m2 = sp.copy(m)
    tv = sp.linspace(-1, 1, num=len(m))
    A = sp.zeros([len(m), polyord])
    for j in range(polyord):
        A[:, j] = tv**(float(j))
    A2 = sp.dot(W,A)
    m2w = sp.dot(m2,W)
    fit = None
    for i in range(n_iter):
        xhat = sp.linalg.lstsq(A2, m2w)[0]
        fit = sp.dot(A, xhat)
        # use gradient for central finite differences which keeps order
        resid = sp.gradient(fit - m2)
        std = sp.std(resid)
        bidx = sp.where(sp.absolute(resid) > 2.0*std)[0]
        for bi in bidx:
            A2[bi,:]=0.0
            m2[bi]=0.0
            m2w[bi]=0.0
    if debug_plot:
        plt.plot(m2,label="outlier removed")
        plt.plot(m,label="original")
        plt.plot(fit,label="fit")
        plt.legend()
        plt.ylim([sp.minimum(fit)-std*3.0,sp.maximum(fit)+std*3.0])
        plt.show()
    return(fit)

def get_html(self, base_file_name: str, h_level: int) -> str:
        html = """
            <h{}>{}</h{}>
        """.format(h_level, self.name, h_level)
        scores = self.get_impl_mean_scores()
        std_devs = self.get_statistical_property_scores(rel_std_dev_func)
        if len(self.programs) > 1:
            html += """
                Mean scores per implementation for this program category
                <p>
            """
            html += self.get_box_plot_html(base_file_name)
            html += """
                </p>
                <table class="table">
                    <tr><th>implementation</th><th>geom mean over means relative to best (per input and program) aka mean score</th>
                    <th>... std devs relative to the best means </th>
                    </tr>
            """
            for impl in scores.keys():
                html += """
                    <tr><td>{}</td><td>{:5.2%}</td><td>{:5.2%}</td></tr>
                """.format(impl, scores[impl], std_devs[impl])
            html += "</table>"
            if len(self.get_input_strs()) > 1:
                html += """
                <h{h}> Mean scores per input</h{h}>
                """.format(h=h_level + 1)
                for input in self.get_input_strs():
                    mean_scores = self.get_statistical_property_scores_per_input_per_impl(rel_mean_func, input)
                    std_scores = self.get_statistical_property_scores_per_input_per_impl(rel_std_dev_func, input)
                    html += """
                        <h{h}>Mean scores for input {!r}</h{h}>
                        The plot shows the distribution of mean scores per program for each implementation.
                        <p>
                    """.format(input, h=h_level + 2)
                    html += self.get_box_plot_per_input_per_impl_html(base_file_name, input)
                    html += """
                        </p>
                        <table class="table">
                            <tr><th>impl</th><th>geom mean over means relative to best (per program) aka mean score</th>
                            <th>... std devs relative to the best means </th>
                            </tr>
                    """
                    for impl in mean_scores.keys():
                        html += """
                            <tr><td>{}</td><td>{:5.2%}</td><td>{:5.2%}</td></tr>
                        """.format(impl, stats.gmean(mean_scores[impl]), stats.gmean(std_scores[impl]))
                    html += "</table>"
        impl_names = list(scores.keys())
        for (i, prog) in enumerate(self.programs):
            html += self.programs[prog].get_html(base_file_name + "_" + html_escape_property(prog), h_level + 1)
        return html

def c_config(inputs_per_category: InputsPerCategory, optimisation: str = "-O2", clang_version = "3.7") -> ConfigDict:
    """
    Generates a game config that compares gcc and clang.
    """

    def cat(category: str, numbers: t.List[int] = None):
        return bench_category(category, "gcc", inputs_per_category[category], numbers)

    config = {
        "language": "c",
        "categories": [
            cat("binarytrees"),
            cat("chameneosredux", [2]),
            cat("fannkuchredux", [1, 5]),
            cat("fasta", [1, 4, 5]),
            cat("fastaredux"),
            #cat("knucleotide", "gcc", [9]) # doesn't compile
            cat("mandelbrot", [1, 2, 3, 4, 6, 9]),
            cat("meteor"),
            cat("nbody"),
            cat("pidigits"),
            #cat("regexdna", "gcc", [1, 2]),      # runs almost infinitely
            cat("revcomp", [1]),
            cat("spectralnorm", [1]),
            cat("threadring")
        ],
        "impls": [
            {
                "name": "gcc", # todo: tcl8.6 vs 8.4???
                "build_cmd": "cp {file} {bfile}.c; gcc {bfile}.c $O -I/usr/include/tcl8.6 -ltcl8.4 -lglib-2.0 -lgmp "
                             "-D_GNU_SOURCE -Doff_t=__off64_t -fopenmp -D_FILE_OFFSET_BITS=64 -I/usr/include/apr-1.0 "
                             "-lapr-1 -lgomp -lm -std=c99 -mfpmath=sse -msse3 -I/usr/include/glib-2.0 "
                             "-I/usr/lib/x86_64-linux-gnu/glib-2.0/include -lglib-2.0 -lpcre -o {bfile}"
                             .replace("$O", optimisation),
                "run_cmd": "./{bfile} {input} > /dev/null"
            }, {
                "name": "clang",
                "build_cmd": "cp {file} {bfile}.c; clang-$CV {bfile}.c $O -I/usr/include/tcl8.6 -ltcl8.4 -fopenmp=libgomp "
                             "-lglib-2.0 -lgmp -D_GNU_SOURCE -Doff_t=__off64_t -D_FILE_OFFSET_BITS=64 "
                             "-I/usr/include/apr-1.0 -lapr-1  -lm -std=c99 -mfpmath=sse -msse3 -I/usr/include/glib-2.0 "
                             "-I/usr/lib/x86_64-linux-gnu/glib-2.0/include -lglib-2.0 -lpcre -o {bfile}"
                            .replace("$CV", clang_version).replace("$O", optimisation),
                "run_cmd": "./{bfile} {input} > /dev/null"
            }
        ]
    }

    return config