python类round()的实例源码

def resample(image, scan, new_spacing=[1,1,1]):
    # Determine current pixel spacing
    spacing = map(float, ([scan[0].SliceThickness] + scan[0].PixelSpacing))
    spacing = np.array(list(spacing))

    #scan[2].SliceThickness


    resize_factor = spacing / new_spacing
    new_real_shape = image.shape * resize_factor
    new_shape = np.round(new_real_shape)
    real_resize_factor = new_shape / image.shape
    new_spacing = spacing / real_resize_factor

    image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest')  ### early orig modified 

    return image, new_spacing

def resample(image, scan, new_spacing=[1,1,1]):
    # Determine current pixel spacing
    spacing = map(float, ([scan[0].SliceThickness] + scan[0].PixelSpacing))
    spacing = np.array(list(spacing))

    resize_factor = spacing / new_spacing
    new_real_shape = image.shape * resize_factor
    new_shape = np.round(new_real_shape)
    real_resize_factor = new_shape / image.shape
    new_spacing = spacing / real_resize_factor

    #image = scipy.ndimage.interpolation.zoom(image, real_resize_factor)   # nor mode= "wrap"/xxx, nor cval=-1024 can ensure that the min and max values are unchanged .... # cval added
    image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest')  ### early orig modified 
    #image = scipy.ndimage.zoom(image, real_resize_factor, order=1)    # order=1 bilinear , preserves the min and max of the image -- pronbably better for us (also faster than spkine/order=2)

    #image = scipy.ndimage.zoom(image, real_resize_factor, mode='nearest', order=1)    # order=1 bilinear , preserves the min and max of the image -- pronbably better for us (also faster than spkine/order=2)

    return image, new_spacing

def handle_data(self, data):
        if self.target_shares == 0:
            assert 0 not in self.portfolio.positions
            self.order(self.sid(0), 10)
            self.target_shares = 10
            return
        else:
            print(self.portfolio)
            assert self.portfolio.positions[0]['amount'] == \
                self.target_shares, "Orders not filled immediately."
            assert self.portfolio.positions[0]['last_sale_price'] == \
                data[0].price, "Orders not filled at current price."

        self.order_target_value(self.sid(0), 20)
        self.target_shares = np.round(20 / data[0].price)

        if isinstance(self.sid(0), Equity):
            self.target_shares = np.round(20 / data[0].price)
        if isinstance(self.sid(0), Future):
            self.target_shares = np.round(
                20 / (data[0].price * self.sid(0).multiplier))

def round_to_chunk_size(self, chunk_size, offset=Vec(0,0,0, dtype=int)):
    """
    Align a potentially non-axis aligned bbox to the grid by rounding it
    to the nearest grid lines.

    Required:
      chunk_size: arraylike (x,y,z), the size of chunks in the 
                    dataset e.g. (64,64,64)
    Optional:
      offset: arraylike (x,y,z), the starting coordinate of the dataset
    """
    chunk_size = np.array(chunk_size, dtype=np.float32)
    result = self.clone()
    result = result - offset
    result.minpt = np.round(result.minpt / chunk_size) * chunk_size
    result.maxpt = np.round(result.maxpt / chunk_size) * chunk_size
    return result + offset

def draw_bounding_boxes(image, gt_boxes, im_info):
  num_boxes = gt_boxes.shape[0]
  gt_boxes_new = gt_boxes.copy()
  gt_boxes_new[:,:4] = np.round(gt_boxes_new[:,:4].copy() / im_info[2])
  disp_image = Image.fromarray(np.uint8(image[0]))

  for i in xrange(num_boxes):
    this_class = int(gt_boxes_new[i, 4])
    disp_image = _draw_single_box(disp_image, 
                                gt_boxes_new[i, 0],
                                gt_boxes_new[i, 1],
                                gt_boxes_new[i, 2],
                                gt_boxes_new[i, 3],
                                'N%02d-C%02d' % (i, this_class),
                                FONT,
                                color=STANDARD_COLORS[this_class % NUM_COLORS])

  image[0, :] = np.array(disp_image)
  return image

def quantized_forward_pass_cost_and_output(inputs, weights, scales, biases=None, quantization_method='round',
        hidden_activations='relu', output_activation = 'relu', computation_calc='adds', seed=None):
    """
    Do a forward pass of a discretized network, and return the (pseudo) computational cost and final output.

    :param inputs: A (n_samples, n_dims) array of inputs
    :param weights: A list of (n_dim_in, n_dim_out) arrays of weights
    :param scales: A list of (w[0].shape[0], w[1].shape[0], ...) scales to multiply/divide by before/after the quantization
    :param quantization_method: The method of quantization/discretization: 'round', 'uniform', None, ....
    :param seed: A random seed or number generator
    :return: n_ops, output_activation: Where:
        n_ops is the (scalar) number of commputations required in the forward pass (only striclty true if scale is 'round', .. otherwise it's some kind of surrogate.
        output_activation: A (n_samples, n_dims) array representing the output activations.
    """
    activations = scaled_quantized_forward_pass(inputs= inputs, weights=weights, biases=biases, scales=scales,
        hidden_activations=hidden_activations, output_activations=output_activation, quantization_method=quantization_method, rng=seed)
    spike_activations = activations[1::3]
    n_ops = sparse_nn_flop_count(spike_activations, [w.shape[1] for w in weights], mode=computation_calc) if quantization_method is not None else None
    return n_ops, activations[-1]

def resample(patient, new_spacing=[1,1,1]):
    scan = get_scan(patient)
    image = get_3D_data(patient)

    # Determine current pixel spacing
    spacing = np.array([scan[0].SliceThickness] + scan[0].PixelSpacing, dtype=np.float32)

    resize_factor = spacing / new_spacing
    new_real_shape = image.shape * resize_factor
    new_shape = np.round(new_real_shape)
    real_resize_factor = new_shape / image.shape
    new_spacing = spacing / real_resize_factor

    image = nd.interpolation.zoom(image, real_resize_factor, mode='nearest')

    return image

# For the sake of testing the network, we'll be using the sample dataset
# For this, we'll use the maximum size of the image
# and PAD any image with -1000 values which is smaller than that

#PS: only the first dimension is different in sample dataset
#which is not the case in actual dataset

def did_succeed( output_dict, cond_dict ):
    '''
    Used in rejection sampling:
    for each row, determine if cond is satisfied
    for every cond in cond_dict

    success is hardcoded as round(label) being exactly equal
    to the integer in cond_dict
    '''

    #definition success:
    def is_win(key):
        #cond=np.squeeze(cond_dict[key])
        cond=np.squeeze(cond_dict[key])
        val=np.squeeze(output_dict[key])
        condition= np.round(val)==cond
        return condition

    scoreboard=[is_win(key) for key in cond_dict]
    #print('scoreboard', scoreboard)
    all_victories_bool=np.logical_and.reduce(scoreboard)
    return all_victories_bool.flatten()

def func_impl(self, x):
        objval, invalid = None, False
        for i, t in enumerate(x):
            if t < self.p_min[i] or t > self.p_max[i]:
                objval = float("inf")
                invalid = True
        if not invalid:
            x = [int(np.round(x_t)) if p_t is "integer" else x_t for p_t, x_t in zip(self.p_types, x)]
            objval = self._coef * self.func(x)

        print("{:5d} | {} | {:>15.5f}".format(
            self.n_eval,
            " | ".join(["{:>15.5f}".format(t) for t in x]),
            self._coef * objval
        ))

        self.n_eval += 1
        return objval

def clean_height(df):
    v = df.VALUE.astype(float)
    idx = df.VALUEUOM.fillna('').apply(lambda s: 'in' in s.lower()) | df.MIMIC_LABEL.apply(lambda s: 'in' in s.lower())
    v.ix[idx] = np.round(v[idx] * 2.54)
    return v

# ETCO2: haven't found yet
# Urine output: ambiguous units (raw ccs, ccs/kg/hr, 24-hr, etc.)
# Tidal volume: tried to substitute for ETCO2 but units are ambiguous
# Glascow coma scale eye opening
# Glascow coma scale motor response
# Glascow coma scale total
# Glascow coma scale verbal response
# Heart Rate
# Respiratory rate
# Mean blood pressure

def test_sample_NormalFloatHyperparameter(self):
        hp = NormalFloatHyperparameter("nfhp", 0, 1)

        def actual_test():
            rs = np.random.RandomState(1)
            counts_per_bin = [0 for i in range(11)]
            for i in range(100000):
                value = hp.sample(rs)
                index = min(max(int((round(value + 0.5)) + 5), 0), 9)
                counts_per_bin[index] += 1

            self.assertEqual([0, 4, 138, 2113, 13394, 34104, 34282, 13683,
                              2136, 146, 0], counts_per_bin)

            return counts_per_bin

        self.assertEqual(actual_test(), actual_test())

def round_solution_pool(pool, constraints):

    pool.distinct().sort()
    P = pool.P
    L0_reg_ind = np.isnan(constraints['coef_set'].C_0j)
    L0_max = constraints['L0_max']
    rounded_pool = SolutionPool(P)

    for solution in pool.solutions:
        # sort from largest to smallest coefficients
        feature_order = np.argsort([-abs(x) for x in solution])
        rounded_solution = np.zeros(shape=(1, P))
        l0_norm_count = 0
        for k in range(0, P):
            j = feature_order[k]
            if not L0_reg_ind[j]:
                rounded_solution[0, j] = np.round(solution[j], 0)
            elif l0_norm_count < L0_max:
                rounded_solution[0, j] = np.round(solution[j], 0)
                l0_norm_count += L0_reg_ind[j]

        rounded_pool.add(objvals=np.nan, solutions=rounded_solution)

    rounded_pool.distinct().sort()
    return rounded_pool

def resize(im, target_size, max_size):
    """
    only resize input image to target size and return scale
    :param im: BGR image input by opencv
    :param target_size: one dimensional size (the short side)
    :param max_size: one dimensional max size (the long side)
    :return:
    """
    im_shape = im.shape
    im_size_min = np.min(im_shape[0:2])
    im_size_max = np.max(im_shape[0:2])
    im_scale = float(target_size) / float(im_size_min)
    # prevent bigger axis from being more than max_size:
    if np.round(im_scale * im_size_max) > max_size:
        im_scale = float(max_size) / float(im_size_max)
    im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR)
    return im, im_scale

def resize(im, target_size, max_size):
    """
    only resize input image to target size and return scale
    :param im: BGR image input by opencv
    :param target_size: one dimensional size (the short side)
    :param max_size: one dimensional max size (the long side)
    :return:
    """
    im_shape = im.shape
    im_size_min = np.min(im_shape[0:2])
    im_size_max = np.max(im_shape[0:2])
    im_scale = float(target_size) / float(im_size_min)
    if np.round(im_scale * im_size_max) > max_size:
        im_scale = float(max_size) / float(im_size_max)
    im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR)
    return im, im_scale

def generateTickText(tickValue, ratio, baseline=False):
    multStep = 1000.
    multipliers = [
        dict(suffix='', mult=pow(multStep, 0)),
        dict(suffix='k', mult=pow(multStep, 1)),
        dict(suffix='M', mult=pow(multStep, 2)),
        dict(suffix='G', mult=pow(multStep, 3)),
    ]
    multiplier = multipliers[0]
    for m in multipliers:
        if np.round(tickValue / m['mult'], decimals=2) >= 1:
            multiplier = m
    baseText = float('%.3g' % np.round(tickValue / multiplier['mult'], decimals=2))
    baseText = int(baseText) if int(baseText) == baseText else baseText
    suffix = multiplier['suffix']
    percent = float('%.1f' % (100 * ratio))
    percent = int(percent) if percent == int(percent) else percent
    return '%s%s [%s%%]' % (baseText, suffix, percent)

def generateTickText(tickValue, ratio, baseline = False):
    multStep = 1000.
    multipliers = [
        dict(suffix='', mult=pow(multStep, 0)),
        dict(suffix='k', mult=pow(multStep, 1)),
        dict(suffix='M', mult=pow(multStep, 2)),
        dict(suffix='G', mult=pow(multStep, 3)),
    ]
    multiplier = multipliers[0]
    for m in multipliers:
        if np.round(tickValue / m['mult']) >= 1:
            multiplier = m
    baseText = float('%.3g' % np.round(tickValue / multiplier['mult']))
    baseText = int(baseText) if int(baseText) == baseText else baseText
    suffix = multiplier['suffix']
    percent = float('%.1f' % (100 * ratio))
    percent = int(percent) if percent == int(percent) else percent
    return '%s%s [%s%%]' % (baseText, suffix, percent)

def apply_regr(x, y, w, h, tx, ty, tw, th):
    try:
        cx = x + w/2.
        cy = y + h/2.
        cx1 = tx * w + cx
        cy1 = ty * h + cy
        w1 = math.exp(tw) * w
        h1 = math.exp(th) * h
        x1 = cx1 - w1/2.
        y1 = cy1 - h1/2.
        x1 = int(round(x1))
        y1 = int(round(y1))
        w1 = int(round(w1))
        h1 = int(round(h1))

        return x1, y1, w1, h1

    except ValueError:
        return x, y, w, h
    except OverflowError:
        return x, y, w, h
    except Exception as e:
        print(e)
        return x, y, w, h

def prep_im_for_blob(im, pixel_means, target_size, max_size):
    """Mean subtract and scale an image for use in a blob."""
    im = im.astype(np.float32, copy=False)
    im -= pixel_means
    im = im / 127.5
    im_shape = im.shape
    im_size_min = np.min(im_shape[0:2])
    im_size_max = np.max(im_shape[0:2])
    im_scale = float(target_size) / float(im_size_min)
    # Prevent the biggest axis from being more than MAX_SIZE
    if np.round(im_scale * im_size_max) > max_size:
        im_scale = float(max_size) / float(im_size_max)
    im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
                    interpolation=cv2.INTER_LINEAR)

    return im, im_scale

def _gene_embed_space(self,vec):
        shape = vec.shape
        vec = vec.flatten()
        combo_neg_idx = np.array([1 if vec[i]<0  else 0 for i in range(len(vec))])

        vec_pos = np.abs(vec)
        int_part = np.floor(vec_pos)
        frac_part = np.round(vec_pos - int_part,2)

        bi_int_part=[] #?????????????signature???????
        for i in range(len(int_part)):
            bi=list(bin(int(int_part[i]))[2:])
            bie = [0] * (16 - len(bi))
            bie.extend(bi)
            bi_int_part.append(np.array(bie,dtype=np.uint16))
        bi_int_part = np.array(bi_int_part)

        sig = []
        for i in range(len(bi_int_part)):
            sig.append(bi_int_part[i][10])
        sig = np.array(sig).reshape(shape)
        return np.array(bi_int_part),frac_part.reshape(shape),combo_neg_idx.reshape(shape),sig

def _gene_embed_space(self,vec):
        shape = vec.shape
        vec = vec.flatten()
        combo_neg_idx = np.array([1 if vec[i]<0  else 0 for i in range(len(vec))])

        vec_pos = np.abs(vec)
        int_part = np.floor(vec_pos)
        frac_part = np.round(vec_pos - int_part,2)

        bi_int_part=[] #?????????????signature???????
        for i in range(len(int_part)):
            bi=list(bin(int(int_part[i]))[2:])
            bie = [0] * (16 - len(bi))
            bie.extend(bi)
            bi_int_part.append(np.array(bie,dtype=np.uint16))
        bi_int_part = np.array(bi_int_part)

        sig = []
        for i in range(len(bi_int_part)):
            sig.append(bi_int_part[i][10])
        sig = np.array(sig).reshape(shape)
        return np.array(bi_int_part),frac_part.reshape(shape),combo_neg_idx.reshape(shape),sig

def crop_pad(image, corner, shape):
    ndim = len(corner)
    corner = [int(round(c)) for c in corner]
    shape = [int(round(s)) for s in shape]
    original = image.shape[-ndim:]
    zipped = zip(corner, shape, original)

    if np.any(c < 0 or c + s > o for (c, s, o) in zipped):
        no_padding = [(0, 0)] * (image.ndim - ndim)
        padding = [(max(-c, 0), max(c + s - o, 0)) for (c, s, o) in zipped]
        corner = [c + max(-c, 0) for c in corner]
        image_temp = np.pad(image, no_padding + padding, mode=str('constant'))
    else:
        image_temp = image

    no_crop = [slice(o+1) for o in image.shape[:-ndim]]
    crop = [slice(c, c+s) for (c, s) in zip(corner, shape)]
    return image_temp[no_crop + crop]

def apply_regr(x, y, w, h, tx, ty, tw, th):
    try:
        cx = x + w/2.
        cy = y + h/2.
        cx1 = tx * w + cx
        cy1 = ty * h + cy
        w1 = math.exp(tw) * w
        h1 = math.exp(th) * h
        x1 = cx1 - w1/2.
        y1 = cy1 - h1/2.
        x1 = int(round(x1))
        y1 = int(round(y1))
        w1 = int(round(w1))
        h1 = int(round(h1))

        return x1, y1, w1, h1

    except ValueError:
        return x, y, w, h
    except OverflowError:
        return x, y, w, h
    except Exception as e:
        print(e)
        return x, y, w, h

def compute_centroids(object_matrix, preserve_ids=False, round_val=False):

    # if ids=true, then write a matrix equal to size of maximum
    # value, else, order in object label order

    # if round = true, round centroid coordinates to nearest integer
    # when rounding, TODO: make sure we don't leave the volume

    import skimage.measure as measure

    centroids = []
    # Threshold data
    rp = measure.regionprops(object_matrix)

    for r in rp:
        if round_val > 0:
            centroids.append(np.round(r.Centroid, round_val))
        else:
            centroids.append(r.Centroid)

    return centroids

def pareto_front(vals1, vals2, round_val=3):

    # butter and guns pareto front.  Removes points not on
    # the pareto frontier

    # round very similar vals
    vals1 = round(vals1, round_val)
    vals2 = round(vals2, round_val)

    v1_out = []
    v2_out = []
    idx_out = []
    for idx in range(0, len(vals1)):

        is_better = np.find(vals1 >= vals1[idx] and vals2 >= vals2[idx])
        if is_better is None:
            v1_out.append(vals1[idx])
            v2_out.append(vals2[idx])
            idx_out.append(idx)

    return v1_out, v2_out, idx_out

def _downsample_mask(X, pct):
    """ Create a boolean mask indicating which subset of X should be
    evaluated.
    """
    if pct < 1.0:
        Mask = np.zeros(X.shape, dtype=np.bool)
        m = X.shape[-2]
        n = X.shape[-1]
        nToEval = np.round(pct*m*n).astype(np.int32)
        idx = sobol(2, nToEval ,0)
        idx[0] = np.floor(m*idx[0])
        idx[1] = np.floor(n*idx[1])
        idx = idx.astype(np.int32)
        Mask[:,:,idx[0], idx[1]] = True
    else:
        Mask = np.ones(X.shape, dtype=np.bool)

    return Mask

def prepare_oae_PU4(known_transisitons):
    print("Learn from pre + action label",
          "*** INCOMPATIBLE MODEL! ***",
          sep="\n")
    N = known_transisitons.shape[1] // 2

    y = generate_oae_action(known_transisitons)

    ind = np.where(np.squeeze(combined(y[:,N:])) > 0.5)[0]

    y = y[ind]

    actions = oae.encode_action(known_transisitons, batch_size=1000).round()
    positive = np.concatenate((known_transisitons[:,:N], np.squeeze(actions)), axis=1)
    actions = oae.encode_action(y, batch_size=1000).round()
    negative = np.concatenate((y[:,:N], np.squeeze(actions)), axis=1)
    # random.shuffle(negative)
    # negative = negative[:len(positive)]
    # normalize
    return (default_networks['PUDiscriminator'], *prepare_binary_classification_data(positive, negative))

def prepare_oae_PU5(known_transisitons):
    print("Learn from pre + suc + action label",
          "*** INCOMPATIBLE MODEL! ***",
          sep="\n")
    N = known_transisitons.shape[1] // 2

    y = generate_oae_action(known_transisitons)

    ind = np.where(np.squeeze(combined(y[:,N:])) > 0.5)[0]

    y = y[ind]

    actions = oae.encode_action(known_transisitons, batch_size=1000).round()
    positive = np.concatenate((known_transisitons, np.squeeze(actions)), axis=1)
    actions = oae.encode_action(y, batch_size=1000).round()
    negative = np.concatenate((y, np.squeeze(actions)), axis=1)
    # random.shuffle(negative)
    # negative = negative[:len(positive)]
    # normalize
    return (default_networks['PUDiscriminator'], *prepare_binary_classification_data(positive, negative))

def puzzle_plot(p):
    p.setup()
    def name(template):
        return template.format(p.__name__)
    from itertools import islice
    configs = list(islice(p.generate_configs(9), 1000)) # be careful, islice is not immutable!!!
    import numpy.random as random
    random.shuffle(configs)
    configs = configs[:10]
    puzzles = p.generate(configs, 3, 3)
    print(puzzles.shape, "mean", puzzles.mean(), "stdev", np.std(puzzles))
    plot_image(puzzles[-1], name("{}.png"))
    plot_image(np.clip(puzzles[-1]+np.random.normal(0,0.1,puzzles[-1].shape),0,1),name("{}+noise.png"))
    plot_image(np.round(np.clip(puzzles[-1]+np.random.normal(0,0.1,puzzles[-1].shape),0,1)),name("{}+noise+round.png"))
    plot_grid(puzzles, name("{}s.png"))
    _transitions = p.transitions(3,3,configs=configs)
    print(_transitions.shape)
    transitions_for_show = \
        np.einsum('ba...->ab...',_transitions) \
          .reshape((-1,)+_transitions.shape[2:])
    print(transitions_for_show.shape)
    plot_grid(transitions_for_show, name("{}_transitions.png"))

def point_trans(ori_point, angle, ori_shape, new_shape):
    """ Transfrom the point from original to rotated image.
    Args:
        ori_point: Point coordinates in original image.
        angle: Rotate angle.
        ori_shape: The shape of original image.
        new_shape: The shape of rotated image.

    Returns:
        Numpy array of new point coordinates in rotated image.
    """
    dx = ori_point[0] - ori_shape[1] / 2.0
    dy = ori_point[1] - ori_shape[0] / 2.0

    t_x = round(dx * math.cos(angle) - dy * math.sin(angle) + new_shape[1] / 2.0)
    t_y = round(dx * math.sin(angle) + dy * math.cos(angle) + new_shape[0] / 2.0)
    return np.array((int(t_x), int(t_y)))

def predict_tf_once(day,start_date = '2016-10-1'):
    all_dataset = get_dataset(day)
    all_dataset = map(lambda x:x.ix[start_date:start_date],all_dataset)
    y_p_features = map(lambda user_id:tf_percent_model.resample_x_y_(all_dataset,user_id)[0].reshape(-1),get_full_user_ids())
    y_p_features_df = pd.DataFrame(y_p_features,index = get_full_user_ids())
    percent = pd.DataFrame.from_csv('./features/tensorflow_model/percent_model/%d.csv'%day)
    #percent = pd.DataFrame.from_csv('./features/tensorflow_model/percent_model/%d.csv'%2)
    #%%
    percent = percent[map(lambda x:'percent#%d'%x,range(_feature_length))]
    t = pd.DataFrame(index = percent.index)
    t[pd.Timestamp(start_date)+pd.Timedelta('%dd'%(day-1))] = (np.array(y_p_features_df)*percent).sum(axis=1)
    t = t.T
    t.to_csv('./result/predict_part/%d.csv'%day)
    real = int(np.round((np.array(y_p_features_df)*percent).sum().sum()))
    print (day,real)
    return (day,real)